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**Summary**: - machine learning is the general term for when computers learn from data - there are lots of different ways ("algorithms") that machines can learn - the algorithms can be grouped into supervised, unsupervised, and reinforcement algorithms* - the data that you feed to a machine learning algorithm can be input-output pairs or just inputs - supervised learning algorithms require input-output pairs (i.e. they require the output) - unsupervised learning requires only the input data (not the outputs) - here is how, in general, supervised algorithms work: - you feed it an example input, then the associated output - you repeat the above step many many times - eventually, the algorithm picks up a pattern between the inputs and outputs - now, you can feed it a brand new input, and it will predict the output for you - here is how, in general, unsupervised algorithms work: - you feed it an example input (without the associated output) - you repeat the above step many times - eventually, the algorithm clusters your inputs into groups - now, you can feed it a brand new input, and the algorithm will predict which cluster it belongs with * the first example in this video used the k-nearest neighbor algorithm, which is a supervised machine learning algorithm Hope that was useful to someone! Thanks for the video, really enjoyed it!! :)
Quite great. An Amazing one explaining the ML basis.!! 1. Supervised learning. 2. Supervised learning after Feedback (Rein inforced learning) 3. Unsupervised learning.
Wow! You got all the answers right. Thanks for your kind comment as well. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
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wow! this is my first time actually researching this topic being a computer science student. i have got to say, this really brightened my mood and brought some light to my day/mind regarding my major! :) awesome stuff!
Exactly! Search engines work based on Machine Learning concepts. Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
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Amazon does this thing where it compares items u buy to other items that other customers have bought as a suggestion to get u to buy the same things other people have bought and it changes some of the time
Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
@@poojaritulasi7680 Hi Poojari, machine learning is used in the various fields now. We recommend you check out the below link to know about Machine Learning and why it matters a lot: www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article.
1. FB case: Supervised scenario (photo tags become labels) 2. Netflix case: Supervised scenario (like and dislike of a movie/show become the label) 3. Bank fraud case: Unsupervised scenario
This video is quiet frankly down to point. I was even excited when I begun this field and the different things you could indulge in and improve for a business. It really is helping me and my career. I am even starting my own channel to breakdown some of the concepts that I found hard to understand about different algorithms and how they work. Check it out and for any starters, do tell me what you find hard at first to grasp when begging into the field ☺️
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Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@@SimplilearnOfficial If you use the decision tree by using existing features to classify a transaction as fraud (1) and no-fraud (0) than you are using a supervised learning based on classification. Right?
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I am confused to figure out how to determine if the data is labeled or not I need to get this concept down first can you help me out with it also I have enrolled in a machine learning course on your website. My ultimate goal is to pursue this and make a career out of it.
"Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course."
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
@@SimplilearnOfficial Why is scenario 3 unsupervised learning? How does the system know that sth is "fraud" without being fed in previous cases which were called "fraud"? Like it has to know the features that make sth "fraud" before it can identify sth as "fraud"
@@angelflyinghigh1300 Hi Lucia, I would recon it (for example) compares properties of many transactions and puts the common ones in groups and thus sees which properties are anomalies (like, really big transaction amounts, or a never used bank account located far away, or many many small transactions with unclear description). But, that's just my two cents, I'm far from knowledgeable of Machine learning :)
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Hi, you almost got it right. Below are the right answers and explanation for the same. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Hi Pratibha, you got all the answers correct. Kudos. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@@SimplilearnOfficial I am a massive fan of visual aids and numerous example driven content and interesting narratives in learning and kudos to SL I love the headfirst set of books which heavily uses stories and visual aids I have a question.I am looking to sign up for a course in AI AND ML. My question is if lectures n SL will be heavily based on visual narrations and interesting examples throughout the course ? IF SO,that would be truly wonderful and clutter breaking
That's great to hear it. Our courses do have visual narrations with 15+ real life industry projects. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/artificial-intelligence-introduction-for-beginners-training-course.
Yes, it is indeed a game changer. Check out our Machine learning playlist to know about the fundamentals courses and algorithms: ruclips.net/video/ukzFI9rgwfU/видео.html. For rest of the course, you need to sign up for our Machine learning Certification Training Course: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
Scenario 1: Facebook face recognition is supervised machine learning....as it takes data from the past to give machine the idea of how this or any persons face would look like...... Scenario 2 netflix movies recommendation is unsupervised machine learning as it does clustering...... Scenario 3 is supervised learning as to detect wether a transaction is fraudulent or not machines need to learn firsts what is fraudulent behaviour......
Amazing amazing video! I have shared with many friends over WhatsApp, can't thank you enough. Quiz answer - scenario 1 is supervised, scenario 2 is supervised, and scenario 3 is unsupervised?
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Thanks for replying to the quiz, Mustafa. You almost got the right answer. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
What a shame these sorts of tools will grow in use. Already humans tend to sameness and lack originality. Now AI's will refine that herd mentality even more, and outliers will get less and less opportunity for patrons and/or interactions. Give these processes ten more years and instead of a Spotify top 10 list that literally has three versions of the same terrible song on it, (this past August) it'll be all 10 as minor variations of the same, soulless crap. Welcome To the Machine.
Hi Amilcar, we are glad that you found our video helpful and informative. Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :).
Hi, you got everything right. Kudos! Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
1) Facebook photo recognition based on tags in an example of supervised learning 2) NetFlix Movie recommendation is an example of unsupervised learning 3) Bank Fraud Detection is an example of reinforcement learning
Amazing video. Thank you Simplilearn. Example where I see application of machine learning could be RUclips itself. Once I watch a video on cooking, all recommendations on cooking video starts popping up!
Here are the answers to the quiz with the explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Haha! That's funny! We hope our videos are being helpful! Do show your love by subscribing our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
Well! First of all thanks for this wonderful and informative video. The answer to the questions in the video might be 1.supeervised 2. supervised 3 . unsupervised Am I correct?
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
A real life problem which may need AI and ML: Examination Paper Evaluation/Correction which has descriptive questions. Two things : The accuracy level of earlier answers can be used to predict the confidence of accuracy of later answers. 2. Based on the other answers, a answer can be evaluated.
Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :). You can also explore our playlists for more Machine Learning Videos - ruclips.net/video/ukzFI9rgwfU/видео.html
an everyday example of machine learning:- Alexa just this song & play the previous one because I don't like this song. Then Alexa removes the song from his recommendation queue & play the previous one.
Hi Anjaney, you almost got everything right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@@SimplilearnOfficial In scenario 3, if you say the suspicious transactions are not defined. Does that means the system might know the valid transaction.?
This means that the model will study the pattern, evaluate whether the transaction done is normal as per the customer history and hence detect a suspicious transaction.
Hi Nitesh, you got everything right. Kudos! Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Hi Onkar, Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Deep Neural network concepts have been implemented for RUclips recommendation. For more detailed explanation, go through this blog: towardsdatascience.com/how-youtube-recommends-videos-b6e003a5ab2f
"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Hi Sitaram, thanks for replying to the quiz. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@@@SimplilearnOfficial fraud transactions to be reinforcement learning right ( as it gives a negative feedback when some enters their data incorrectly )
Please share your feedback and comment below some interesting everyday examples around you where machines are learning and doing amazing jobs. Do not forget to attempt the quiz (05:24). We will give out the answers to the quiz on Wednesday, 26th September 2018 in this same comment! Happy Learning!
Hi Minxin, Below are the right answers and explanation for the quiz. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'
Hi Neha, Below are the right answers and explanation for the quiz. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'
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Hi Isha, you almost got everything right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
You almost got all the answers right. Here are the answers with an explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
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In my point of view 1- Scenario will be using the reinforcement learning. the reason is in the reinforcement example which is explained based on that only i am telling. 2 - scenario will be using the supervised learning. 3 - scenario will be using the unsupervised learning. If it's wrong please correct me. Thanks Simplilearn
Thanks for replying to the quiz Chaitanya. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Hi Sagar, you got everything right. Kudos! Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
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"Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
@Simplilearn Thank you for this video! Shows the power of simplicity and your ability to simplify things. And asking people to comment on the 3 scenarios, great engagement strategy! 🙂
Wow! You got all the answers right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Scenario 1 is supervised earning because machine know the data(both friend photo and their name). 2. Netflix is same as person identify song (high intensity high tempo ) 3. Fraud is unsupervised I guess. By the way video is good. It's wow in one word
Hi Amogha, you are absolutely right about your answer and explanation. We really appreciate your kind comment. Do show your love by subscribing our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
@@SimplilearnOfficial Hi a quick question, should the 3rd one be case of reinforcement learning because transactions are very important and there needs to be a feedback mechanism to recorrect if there is a false positive or false negative ?
@@SimplilearnOfficial Because of the negative side of this. We are feeding a monster we think we have under control. I hope I am wrong but we'll see in a couple of years.
Hi Charly, thanks for your feedback. You can check this link for more insights: theconversation.com/worried-about-ai-taking-over-the-world-you-may-be-making-some-rather-unscientific-assumptions-103561.
"Yes, you are certainly eligible to get into the machine learning field. This Machine Learning course requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial but not mandatory. To kickstart, start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html This playlist will provide you with a solid basic knowledge of Machine learning and its types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-coursea."
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Hi Raja, you almost got everything right. Here are the answers with explanation. Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning. Scenario 2: Recommending new songs based on someone’s past music choices Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs). This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features. Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
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G.
**Summary**:
- machine learning is the general term for when computers learn from data
- there are lots of different ways ("algorithms") that machines can learn
- the algorithms can be grouped into supervised, unsupervised, and reinforcement algorithms*
- the data that you feed to a machine learning algorithm can be input-output pairs or just inputs
- supervised learning algorithms require input-output pairs (i.e. they require the output)
- unsupervised learning requires only the input data (not the outputs)
- here is how, in general, supervised algorithms work:
- you feed it an example input, then the associated output
- you repeat the above step many many times
- eventually, the algorithm picks up a pattern between the inputs and outputs
- now, you can feed it a brand new input, and it will predict the output for you
- here is how, in general, unsupervised algorithms work:
- you feed it an example input (without the associated output)
- you repeat the above step many times
- eventually, the algorithm clusters your inputs into groups
- now, you can feed it a brand new input, and the algorithm will predict which cluster it belongs with
* the first example in this video used the k-nearest neighbor algorithm, which is a supervised machine learning algorithm
Hope that was useful to someone!
Thanks for the video, really enjoyed it!! :)
Wow! This is one of the best summaries!
Thanks for the valuable input!
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@@SimplilearnOfficial Thank you! Definitely will, I love you guys' videos! :) Great job and keep it up!
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i need help
Yes, what could we do for you?
"Hey Siri, can you remind me to book a cab at 6 pm today?"
"Here's what i found on the web for Keanu Reeves' Sixteenth Birthday"
😐
😂😂
Lol
😂😂
:)
Ha ha I think kelvin plank law...
youtube recommended videos are the biggest example of machine learning , bcoz it recommends us videos on the basis of our history. AM I CORRECT?
Yes, you are absolutely correct. Search engine uses Machine learning algorithm to do the recommendation system. Thanks.
And that is what machine learning does
Quite great. An Amazing one explaining the ML basis.!!
1. Supervised learning.
2. Supervised learning after Feedback (Rein inforced learning)
3. Unsupervised learning.
Wow! You got all the answers right. Thanks for your kind comment as well. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Literally learnt more from you than 4 years in college
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Is machine learning this much interesting in college also
…yet you still misspelled ‘learned,’ if only there was a video for that…
😁👍
/
Labeled =supervised
Unlabeled= Un-supervised
And finally
Enforcement Learning = Learning from results and upgrading . Tq for the explanation
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I am from a health care background, but I could effortlessly understand everything she said. Excellent introduction.
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Hi
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Simplilearn I want to expertise on machine learning and succeed in this field. Email : kazis.shafi@gmail.com
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examples of machine learning: weather prediction, prediction of natural events, share price prediction, recommendation system etc.
You are right about the examples. Nice thought!
S-1 supervise learning
s-2 unsupervise learning
s-3 reinforcememt learning
wow! this is my first time actually researching this topic being a computer science student. i have got to say, this really brightened my mood and brought some light to my day/mind regarding my major! :) awesome stuff!
Glad you enjoyed it! Thank you for watching!
In RUclips, It can display the videos as per our frequent past search.
Exactly! Search engines work based on Machine Learning concepts. Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html
This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
Or your likes or dislikes after watching them.
I'm impressed by the way you taught. Teacher should to be like you.
We are glad you found our video helpful, Adnan. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
@@SimplilearnOfficial yes, already did. Thanks.🙏
@@AdnanKhan-iz9zb e3
@@AdnanKhan-iz9zb e3
@@SimplilearnOfficial re Jo inIn
Amazon does this thing where it compares items u buy to other items that other customers have bought as a suggestion to get u to buy the same things other people have bought and it changes some of the time
I got impressed by this tutorial and interested to learn Machine Learning.. Can you guide me..
Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html
This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
What is the use of machine learning .iam looking for good soft ware
@@poojaritulasi7680 Hi Poojari, machine learning is used in the various fields now. We recommend you check out the below link to know about Machine Learning and why it matters a lot: www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article.
1. FB case: Supervised scenario (photo tags become labels)
2. Netflix case: Supervised scenario (like and dislike of a movie/show become the label)
3. Bank fraud case: Unsupervised scenario
Thank you for watching our video!
This video is quiet frankly down to point. I was even excited when I begun this field and the different things you could indulge in and improve for a business. It really is helping me and my career. I am even starting my own channel to breakdown some of the concepts that I found hard to understand about different algorithms and how they work. Check it out and for any starters, do tell me what you find hard at first to grasp when begging into the field ☺️
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Amazing video!! Thanks for sharing the knowledge.
The answers are :
1.Supervised
2.Supervised
3.Unsupervised, right?
Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@@SimplilearnOfficial If you use the decision tree by using existing features to classify a transaction as fraud (1) and no-fraud (0) than you are using a supervised learning based on classification. Right?
Yes, a decision tree is a supervised learning algorithm and is it used for classification problems."
I think...
Scenario 1. Supervised Learning
Scenario 2. Supervised Learning
Scenario 3. Unsupervised Learning
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I am confused to figure out how to determine if the data is labeled or not I need to get this concept down first can you help me out with it also I have enrolled in a machine learning course on your website. My ultimate goal is to pursue this and make a career out of it.
"Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html
This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course."
Scenario-1: supervised
Scenario-2: supervised
Scenario-2: unsupervised
Am i correct,mam?
"Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
@@SimplilearnOfficial Why is scenario 3 unsupervised learning? How does the system know that sth is "fraud" without being fed in previous cases which were called "fraud"? Like it has to know the features that make sth "fraud" before it can identify sth as "fraud"
Simplilearn 🙌🏻
what i thought too
@@angelflyinghigh1300 Hi Lucia, I would recon it (for example) compares properties of many transactions and puts the common ones in groups and thus sees which properties are anomalies (like, really big transaction amounts, or a never used bank account located far away, or many many small transactions with unclear description). But, that's just my two cents, I'm far from knowledgeable of Machine learning :)
We can analyse the comments like machine learning to find answers 😁😁
You got it, what is machine learning hahaha.
Lol...
Well explained by this video :)
Scenario 1: Supervised Learning.
Scenario 2: Supervised Learning.
Scenario 3: Unsupervised Learning.
"Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
1.Reinforcement learning
2.Supervised learning
3.Unsupervised learning
Hi, you almost got it right. Below are the right answers and explanation for the same.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Machine learning helps in catch fraud in unreal hand writing, signatures.
yeah wow!!! you explained so nice...😍😍
ans is 1. super
2. super
3.unsuper
am i correct???
Hi Pratibha, you got all the answers correct. Kudos.
Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@@SimplilearnOfficial I am a massive fan of visual aids and numerous example driven content and interesting narratives in learning and kudos to SL
I love the headfirst set of books which heavily uses stories and visual aids
I have a question.I am looking to sign up for a course in AI AND ML.
My question is if lectures n SL will be heavily based on visual narrations and interesting examples throughout the course ?
IF SO,that would be truly wonderful and clutter breaking
That's great to hear it. Our courses do have visual narrations with 15+ real life industry projects. If you are interested to take up a more structured and formal course, you can find the details here: www.simplilearn.com/artificial-intelligence-introduction-for-beginners-training-course.
Machine learning is a game changer 📈
Yes, it is indeed a game changer. Check out our Machine learning playlist to know about the fundamentals courses and algorithms: ruclips.net/video/ukzFI9rgwfU/видео.html. For rest of the course, you need to sign up for our Machine learning Certification Training Course: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
Want to Enroll & Get Certified ,, Who are best institute in NCR with affordable Price with high placement
Machine learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning. You can start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html
This playlist will provide you with the solid basic knowledge of Machine learning and it types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course.
Scenario 1: Supervised Learning
Scenario 2: Reinforcement Learning
Scenario 3: Unsupervised Learning
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Scenario 1: Facebook face recognition is supervised machine learning....as it takes data from the past to give machine the idea of how this or any persons face would look like......
Scenario 2 netflix movies recommendation is unsupervised machine learning as it does clustering......
Scenario 3 is supervised learning as to detect wether a transaction is fraudulent or not machines need to learn firsts what is fraudulent behaviour......
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Amazing amazing video! I have shared with many friends over WhatsApp, can't thank you enough.
Quiz answer - scenario 1 is supervised, scenario 2 is supervised, and scenario 3 is unsupervised?
Hi Pooja, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Thank you pooja for your answers it helped me to understand
Facebook face recognition with tagged data - Supervised learning
Movie recommendation - Unsupervised
Fraud detection - Unsupervised
Thanks for replying to the quiz, Mustafa. You almost got the right answer. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Loved the video..it's very informative and insightful under 8 mins..
Quiz Answers: 1st and 2nd are supervised while 3rd is unsupervised
Hi Avijeet, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
What a shame these sorts of tools will grow in use. Already humans tend to sameness and lack originality. Now AI's will refine that herd mentality even more, and outliers will get less and less opportunity for patrons and/or interactions. Give these processes ten more years and instead of a Spotify top 10 list that literally has three versions of the same terrible song on it, (this past August) it'll be all 10 as minor variations of the same, soulless crap. Welcome To the Machine.
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What is the name of the software used to create this presentation? 🙏
wonderful and fantastic tutorial! It's really helpful. The explanation is so clear. thumb up to the tutor.
Hi Amilcar, we are glad that you found our video helpful and informative. Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :).
I used supervised learning to decide:
1. Supervised.
2. Supervised.
3. Unsupervised.
Hi, you got everything right. Kudos!
Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
The recommended videos which we are getting in the RUclips PAGE is one of the live examples of machine learning !!
You are right about that!
Supervised learning
Reinforcement Learning
Unsupervised Learning
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scenario 1 is supervised learning
scenario 2 is supervised learning
scenario 3 is unsupervised learning
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1) Facebook photo recognition based on tags in an example of supervised learning
2) NetFlix Movie recommendation is an example of unsupervised learning
3) Bank Fraud Detection is an example of reinforcement learning
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Amazing video. Thank you Simplilearn. Example where I see application of machine learning could be RUclips itself. Once I watch a video on cooking, all recommendations on cooking video starts popping up!
Glad you enjoyed it!
Wonderful video, it's made in such a way that a layman can also understand this..thanks a ton.. please share the answer of that quiz
Hi Bhawna, we are glad that you like our videos! We will give out the answers to the quiz on Wednesday, 26th September 2018.
Here are the answers to the quiz with the explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Answers
Scenario 1 - Supervised
Scenario 2 - Supervised
Scenario 3 - Unsupervised
Please tell me if I am correct or not. Thank Simplilearn !
"Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
UNSUPERVISED
UNSUPERVISED
SUPERVISED
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Respected ma'am, the video was highly informative. Thank you ma'am for teaching so many concepts about machines😄😄
Hello, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
Please help me to learn more ...My Email Id is salaudeen03041969@gmail.com
Oh my god. When you said “Hey Siri” my Siri responded.
Haha! That's funny!
We hope our videos are being helpful! Do show your love by subscribing our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
Same lol and it doesn't even respond well to my own voice
Haha! Funny to hear it!
Gotta fix Siri 🤧 It just responded while I was watching your video on my iPad!
You should try the latest version..
Well! First of all thanks for this wonderful and informative video.
The answer to the questions in the video might be 1.supeervised 2. supervised 3 . unsupervised
Am I correct?
Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Mudit Goyal Dumbass , 1 is supervised not supeervised
How are you replying everybody? Are you a ML model?
What the difference between traditional statistical model with machine learning
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A real life problem which may need AI and ML: Examination Paper Evaluation/Correction which has descriptive questions. Two things : The accuracy level of earlier answers can be used to predict the confidence of accuracy of later answers. 2. Based on the other answers, a answer can be evaluated.
It is certainly a good use case for Machine Learning.
What software is this video made of??curious,please apply me ~
Hi, We use scribe and Adobe aftereffects to make this video. Do subscribe to our channel and stay connected.
Thank you for such a good explanation!
Greetings! Thank you for your kind words. Spread the word by liking, sharing and subscribing to our channel! Cheers :). You can also explore our playlists for more Machine Learning Videos - ruclips.net/video/ukzFI9rgwfU/видео.html
I liked you're videos it's interesting and i can understand it better thanks for the video
Glad it was helpful!
an everyday example of machine learning:- Alexa just this song & play the previous one because I don't like this song. Then Alexa removes the song from his recommendation queue & play the previous one.
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1 - Unsupervised because FB checks your friends face using image recognition
2 - Supervised
3 - Unsupervised
Is this right?
Hi Anjaney, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
Hi Anjaney, you almost got everything right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@@SimplilearnOfficial In scenario 3, if you say the suspicious transactions are not defined. Does that means the system might know the valid transaction.?
This means that the model will study the pattern, evaluate whether the transaction done is normal as per the customer history and hence detect a suspicious transaction.
@@SimplilearnOfficial There is a mistake on the answer, Netflix uses AutoEnconders, and it is unsupervised learning...
To me the 3 scenarios looks like
1. Supervised
2. Supervised
3. Unsupervised
Hi Nitesh, you got everything right. Kudos!
Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Why sir scenario one has supervised lwarning
Hi Onkar,
Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
And if photo is not tagged ..?
It will come under unsupervised learning.
RUclips recommends and shows the type of videos based on which we watched before.Which type of learning is happening here?Can anyone explain?
Deep Neural network concepts have been implemented for RUclips recommendation. For more detailed explanation, go through this blog: towardsdatascience.com/how-youtube-recommends-videos-b6e003a5ab2f
Hi madam exlent explain but small requst Telugu lo explain chayyandi
Hi, we regret to say that it is not possible to make the content in Telugu as we have global audience base. Thanks for understanding.
What is the work of EEE student in machine learning or artificial intelligence
Sorry, We didn’t catch that. Would you mind elaborating your query?
Scenario-1: supervised
Scenario-2: supervised
Scenario-2: unsupervised
"Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Facebook face recognition : supervised , netflex movie choice: reinforced , fraud detection : reinforced
Hi Sitaram, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
Hi Sitaram, thanks for replying to the quiz. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
@@SimplilearnOfficial thank you for the beautiful explanations!!
You are very welcome! Do subscribe to our channel and stay tuned!
@@@SimplilearnOfficial fraud transactions to be reinforcement learning right ( as it gives a negative feedback when some enters their data incorrectly )
Please share your feedback and comment below some interesting everyday examples around you where machines are learning and doing amazing jobs.
Do not forget to attempt the quiz (05:24). We will give out the answers to the quiz on Wednesday, 26th September 2018 in this same comment! Happy Learning!
Simplilearn Hi, I still don't see the answer? :)
Hi Minxin, Below are the right answers and explanation for the quiz.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'
Simplilearn Thanx xD! It is very useful!
You are welcome!
@@SimplilearnOfficial thank you so much well explained
Genetic algorithms,fiol,reinforcement learning ,qlearning topics ?
Stick around as we have a tutorial on the same coming up soon.
1 supervised learning
2 supervised learning
3 unsupervised learning
Scenario 1 - Supervised Learning,
Scenario 2 - Reinforcement Learning,
Scenario 3 - UnSupervised Learning
Hi Neha, Below are the right answers and explanation for the quiz.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'
You guys at Simplilearn are doing great service by making these educational videos. It helps me a lot.
Hey Dipendra, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Great video, very easy to understand. Thanks Simplilearn....
We are glad you found our video helpful, Maini. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
It is very good and information but i did not understand suprised and unsupervised
Thank you for the appreciation. You can check our videos related to various technologies and subscribe to our channel to stay updated with all the trending technologies.
This is very confusing and I didn't learn any thing. Fml. 🙄
Hi, sorry to hear that. Could you please explain a bit more on what has gone wrong. So that we can convey it to the concerned team. Thanks!
It's very easy to understand how ML algorithms work. Thanks for it.
Hey Sanjeev, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Scenario 1 supervised
Scenario 2 reinforced
Scenario 3 unsupervised
Hi Isha, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
Hi Isha, you almost got everything right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Awesome, I am glad to watch this video about Machine Learning. Such a simple and clear explanation. Thank you!
Glad it was helpful!
Senario 1 is supervised
Senario 2 is unsupervised
Senario 3 is unsupervised
You almost got all the answers right. Here are the answers with an explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
Excellent summary. I have shared this with all my linkedin connections.
Much appreciated!
The video was quite interesting and informative. I would like to be your part of learning ML.
Hi Mainak, we are glad you found our video helpful and informative. Do show your love by subscribing our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
In my point of view 1- Scenario will be using the reinforcement learning. the reason is in the reinforcement example which is explained based on that only i am telling.
2 - scenario will be using the supervised learning.
3 - scenario will be using the unsupervised learning.
If it's wrong please correct me.
Thanks Simplilearn
Hi Chaithanya, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
Thanks for replying to the quiz Chaitanya. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Thanks for your answers and correcting me where did some mistake in quiz but I learned it thank you so much simplilearn
You are very welcome Chaitanya. Do subscribe to the channel and stay tuned.
@Simplilearn , wonderful and fantastic tutorial! It's really helpful
1,2 are supervised learning and 3 one is unsupervised
Glad it was helpful!
Do Phone Pay and Google Pay use machine learning? And if so, how?
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It was a wonderful video which make me to Learn it very easy
Glad you liked it!
1->supervised
2->supervised
3->unsupervised
Hi Sagar, you got everything right. Kudos!
Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labeled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious-looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
1st & 2nd -supervised learning
3rd is Reinforced learning.
Thanku , you teach us great 🙏
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
A great gratitude towards simplilearn...really informative video...☺
Hey Manasi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
1 and 3 UNSUPERVISED whereas 2 supervised
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Can someone please tell which software they used to make this video? I am talking about visuals.
Hi, we use Videscribe software tool and Adobe premiere pro to make these videos. Thanks.
You're doing great job, keep it up
From which Nation u r?
Hi, we are online e-learning company with a worldwide presence. For more info, you can visit our website - www.simplilearn.com.
RUclips itself a best example.. is it not? Unsupervised learning... Sometimes reinforce
You are so right about it!
Simple and very easy to understand 👍
Glad to hear that
Scenario 1 : Supervised learning
Scenario 2 : Supervised Learning
Scenario 3 : Unsupervised learning
"Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of ""fraud"" and ""not fraud"". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'."
@@SimplilearnOfficial This is 2 year old Video, and you still reply..
❤❤❤❤
Now i subscribed.. Keep it up..
#stay_connected
keep replying comments
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Really a great and oversimplified video.
Glad it helped!
@Simplilearn Thank you for this video! Shows the power of simplicity and your ability to simplify things. And asking people to comment on the 3 scenarios, great engagement strategy! 🙂
Glad it was helpful!
Umm 1st is supervised, 2nd also supervised, 3rd is unsupervised. Am i correct?
Great video though, loved it!!
Hi Apeksha, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
Wow! You got all the answers right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
Scenario 1 is supervised earning because machine know the data(both friend photo and their name).
2. Netflix is same as person identify song (high intensity high tempo )
3. Fraud is unsupervised I guess.
By the way video is good. It's wow in one word
Hi Amogha, you are absolutely right about your answer and explanation. We really appreciate your kind comment. Do show your love by subscribing our channel using this link: ruclips.net/user/Simplilearn and don't forget to hit the like button as well. Cheers!
@@SimplilearnOfficial Hi a quick question, should the 3rd one be case of reinforcement learning because transactions are very important and there needs to be a feedback mechanism to recorrect if there is a false positive or false negative ?
I have exam tomorrow, and this just one video boosted my confidence to write the exam well with your easy explanations...😊
Hello thank you for watching our video .We are glad that we could help you in your learning !
This is scaring...
Why do you feel so?
@@SimplilearnOfficial Because of the negative side of this. We are feeding a monster we think we have under control. I hope I am wrong but we'll see in a couple of years.
Hi Charly, thanks for your feedback. You can check this link for more insights: theconversation.com/worried-about-ai-taking-over-the-world-you-may-be-making-some-rather-unscientific-assumptions-103561.
Is it benefit qa ? I mean can a tester learn it for career growth?
"Yes, you are certainly eligible to get into the machine learning field. This Machine Learning course requires an understanding of basic statistics and mathematics at the college level. Familiarity with Python programming is also beneficial but not mandatory.
To kickstart, start with this amazing playlist which helped a lot of people: ruclips.net/video/ukzFI9rgwfU/видео.html
This playlist will provide you with a solid basic knowledge of Machine learning and its types with examples. It has videos both in R and Python. If you want to go further and get certified in Machine learning, check this out: www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-coursea."
these examples are so helpful, thanks for making this video! YOU ROCK!
Hi Victoria, we are glad you found our video helpful. Do subscribe to our channel and get our new video updates directly into your email. If you have any questions related to these videos, you can post in the comments section, we will clear your queries/doubts.
You cleared my chart doubts in a single video
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1 & 3 is unsupervised and 2 is supervised, Let us know the answer
Hi Raja, thanks for your reply! We will give out the answers to the quiz on Wednesday, 26th September 2018.
Hi Raja, you almost got everything right. Here are the answers with explanation.
Scenario 1: Facebook recognizes your friend in a picture from an album of tagged photographs
Explanation: It is supervised learning. Here Facebook is using tagged photos to recognize the person. Therefore, the tagged photos become the labels of the pictures and we know that when the machine is learning from labelled data, it is supervised learning.
Scenario 2: Recommending new songs based on someone’s past music choices
Explanation: It is supervised learning. The model is training a classifier on pre-existing labels (genres of songs).
This is what Netflix, Pandora, and Spotify do all the time, they collect the songs/movies that you like already, evaluate the features based on your likes/dislikes and then recommend new movies/songs based on similar features.
Scenario 3: Analyze bank data for suspicious looking transactions and flag the fraud transactions
Explanation: It is unsupervised learning. In this case, the suspicious transactions are not defined, hence there are no labels of "fraud" and "not fraud". The model tries to identify outliers by looking at anomalous transactions and flags them as 'fraud'.
very informative. thanks
We appreciate your kind comment. Do subscribe to our channel and stay tuned!
youtube recommendation
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