Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Machine Learning & AI Masters Course Curriculum, Visit our Website: bit.ly/2QixjBC (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") Here is the video timeline: 2:47 What is Machine Learning? 4:08 AI vs ML vs Deep Learning 5:43 How does Machine Learning works? 6:18 Types of Machine Learning 6:43 Supervised Learning 8:38 Supervised Learning Examples 11:49 Unsupervised Learning 13:54 Unsupervised Learning Examples 16:09 Reinforcement Learning 18:39 Reinforcement Learning Examples 19:34 AI vs Machine Learning vs Deep Learning 22:09 Examples of AI 23:39 Examples of Machine Learning 25:04 What is Deep Learning? 25:54 Example of Deep Learning 27:29 Machine Learning vs Deep Learning 33:49 Jupyter Notebook Tutorial 34:49 Installation 50:24 Machine Learning Tutorial 51:04 Classification Algorithm 51:39 Anomaly Detection Algorithm 52:14 Clustering Algorithm 53:34 Regression Algorithm 54:14 Demo: Iris Dataset 1:12:11 Stats & Probability for Machine Learning 1:16:16 Categories of Data 1:16:36 Qualitative Data 1:17:51 Quantitative Data 1:20:55 What is Statistics? 1:23:25 Statistics Terminologies 1:24:30 Sampling Techniques 1:27:15 Random Sampling 1:28:05 Systematic Sampling 1:28:35 Stratified Sampling 1:29:35 Types of Statistics 1:32:21 Descriptive Statistics 1:37:36 Measures of Spread 1:44:01 Information Gain & Entropy 1:56:08 Confusion Matrix 2:00:53 Probability 2:03:19 Probability Terminologies 2:04:55 Types of Events 2:05:35 Probability of Distribution 2:10:45 Types of Probability 2:11:10 Marginal Probability 2:11:40 Joint Probability 2:12:35 Conditional Probability 2:13:30 Use-Case 2:17:25 Bayes Theorem 2:23:40 Inferential Statistics 2:24:00 Point Estimation 2:26:50 Interval Estimate 2:30:10 Margin of Error 2:34:20 Hypothesis Testing 2:41:25 Supervised Learning Algorithms 2:42:40 Regression 2:44:05 Linear vs Logistic Regression 2:49:55 Understanding Linear Regression Algorithm 3:11:10 Logistic Regression Curve 3:18:34 Titanic Data Analysis 3:58:39 Decision Tree 3:58:59 what is Classification? 4:01:24 Types of Classification 4:08:35 Decision Tree 4:14:20 Decision Tree Terminologies 4:18:05 Entropy 4:44:05 Credit Risk Detection Use-case 4:51:45 Random Forest 5:00:40 Random Forest Use-Cases 5:04:29 Random Forest Algorithm 5:16:44 KNN Algorithm 5:20:09 KNN Algorithm Working 5:27:24 KNN Demo 5:35:05 Naive Bayes 5:40:55 Naive Bayes Working 5:44:25Industrial Use of Naive Bayes 5:50:25 Types of Naive Bayes 5:51:25 Steps involved in Naive Bayes 5:52:05 PIMA Diabetic Test Use Case 6:04:55 Support Vector Machine 6:10:20 Non-Linear SVM 6:12:05 SVM Use-case 6:13:30 k Means Clustering & Association Rule Mining 6:16:33 Types of Clustering 6:17:34 K-Means Clustering 6:17:59 K-Means Working 6:21:54 Pros & Cons of K-Means Clustering 6:23:44 K-Means Demo 6:28:44 Hirechial Clustering 6:31:14 Association Rule Mining 6:34:04 Apriori Algorithm 6:39:19 Apriori Algorithm Demo 6:43:29 Reinforcement Learning 6:46:39 Reinforcement Learning: Counter-Strike Example 6:53:59 Markov's Decision Process 6:58:04 Q-Learning 7:02:39 The Bellman Equation 7:12:14 Transitioning to Q-Learning 7:17:29 Implementing Q-Learning 7:23:33 Machine Learning Projects 7:38:53 Who is a ML Engineer? 7:39:28 ML Engineer Job Trends 7:40:43 ML Engineer Salary Trends 7:42:33 ML Engineer Skills 7:44:08 ML Engineer Job Description 7:45:53 ML Engineer Resume 7:54:48 Machine Learning Interview Questions
00:00 Introduction 2:47 What is Machine Learning? 4:08 AI vs ML vs Deep Learning 5:43 How does Machine Learning works? 6:18 Types of Machine Learning 6:43 Supervised Learning 8:38 Supervised Learning Examples 11:49 Unsupervised Learning 13:54 Unsupervised Learning Examples 16:09 Reinforcement Learning 18:39 Reinforcement Learning Examples 19:34 AI vs Machine Learning vs Deep Learning 22:09 Examples of AI 23:39 Examples of Machine Learning 25:04 What is Deep Learning? 25:54 Example of Deep Learning 27:29 Machine Learning vs Deep Learning 33:49 Jupyter Notebook Tutorial 34:49 Installation 50:24 Machine Learning Tutorial 51:04 Classification Algorithm 51:39 Anomaly Detection Algorithm 52:14 Clustering Algorithm 53:34 Regression Algorithm 54:14 Demo: Iris Dataset 1:12:11 Stats & Probability for Machine Learning 1:16:16 Categories of Data 1:16:36 Qualitative Data 1:17:51 Quantitative Data 1:20:55 What is Statistics? 1:23:25 Statistics Terminologies 1:24:30 Sampling Techniques 1:27:15 Random Sampling 1:28:05 Systematic Sampling 1:28:35 Stratified Sampling 1:29:35 Types of Statistics 1:32:21 Descriptive Statistics 1:37:36 Measures of Spread 1:44:01 Information Gain & Entropy 1:56:08 Confusion Matrix 2:00:53 Probability 2:03:19 Probability Terminologies 2:04:55 Types of Events 2:05:35 Probability of Distribution 2:10:45 Types of Probability 2:11:10 Marginal Probability 2:11:40 Joint Probability 2:12:35 Conditional Probability 2:13:30 Use-Case 2:17:25 Bayes Theorem 2:23:40 Inferential Statistics 2:24:00 Point Estimation 2:26:50 Interval Estimate 2:30:10 Margin of Error 2:34:20 Hypothesis Testing 2:41:25 Supervised Learning Algorithms 2:42:40 Regression 2:44:05 Linear vs Logistic Regression 2:49:55 Understanding Linear Regression Algorithm 3:11:10 Logistic Regression Curve 3:18:34 Titanic Data Analysis 3:58:39 Decision Tree 3:58:59 what is Classification? 4:01:24 Types of Classification 4:08:35 Decision Tree 4:14:20 Decision Tree Terminologies 4:18:05 Entropy 4:44:05 Credit Risk Detection Use-case 4:51:45 Random Forest 5:00:40 Random Forest Use-Cases 5:04:29 Random Forest Algorithm 5:16:44 KNN Algorithm 5:20:09 KNN Algorithm Working 5:27:24 KNN Demo 5:35:05 Naive Bayes 5:40:55 Naive Bayes Working 5:44:25Industrial Use of Naive Bayes 5:50:25 Types of Naive Bayes 5:51:25 Steps involved in Naive Bayes 5:52:05 PIMA Diabetic Test Use Case 6:04:55 Support Vector Machine 6:10:20 Non-Linear SVM 6:12:05 SVM Use-case 6:13:30 k Means Clustering & Association Rule Mining 6:16:33 Types of Clustering 6:17:34 K-Means Clustering 6:17:59 K-Means Working 6:21:54 Pros & Cons of K-Means Clustering 6:23:44 K-Means Demo 6:28:44 Hierarchical Clustering 6:31:14 Association Rule Mining 6:34:04 Apriori Algorithm 6:39:19 Apriori Algorithm Demo 6:43:29 Reinforcement Learning 6:46:39 Reinforcement Learning: Counter-Strike Example 6:53:59 Markov's Decision Process 6:58:04 Q-Learning 7:02:39 The Bellman Equation 7:12:14 Transitioning to Q-Learning 7:17:29 Implementing Q-Learning 7:23:33 Machine Learning Projects 7:38:53 Who is a ML Engineer? 7:39:28 ML Engineer Job Trends 7:40:43 ML Engineer Salary Trends 7:42:33 ML Engineer Skills 7:44:08 ML Engineer Job Description 7:45:53 ML Engineer Resume 7:54:48 Machine Learning Interview Questions
Wow . This is is one of the best ML youtube courses i have ever attended. Its a job well done , its informative , it teaches you everything u wanna know about ML in one short. Thanks guys a job well done!
Thanks for the compliment! We are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
I have completed python for beginner 7hr course and data science and nowthis is 3rd best course thanks edureka I must recommend every user to watch python for beginner
I greatly want to thank you for making this video as it incorporates the perfect necessities to learn about ML and automation. I am currently trying to implement this for my app and it has been a real lifesaver.
One of the most clear explanations about Machine Learning on internet. Thanks for the course , it really helped me in my Data science career. Only one request can you please share the datasheet and the code?
Hi Mohammad, thanks for watching the video. We will definitely look into your suggestions. Do subscribe to our channel and stay connected with us. Cheers!
Thanks for the awesome tutorial, very informative. When I run the code in "54:14 Demo: Iris Dataset ", everything went well until the part that i was supposed to run the algorithms and test the accuracy. The code gave me error. Kindly find below the code, which part is wrong? # Evaluate each model in turn results = [] names = [] for name, model in models: kfold = model_selection.KFold(n_splits=10, random_state=seed) cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) results.append(cv_results) names.append(name) msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()) print(msg) When I run the above code, it gives this error ValueError: Setting a random_state has no effect since shuffle is False. You should leave random_state to its default (None), or set shuffle=True.
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
@edureka! Thanks so much for this amazing piece of intellectual resource. You guys are making complex stuffs look so easy. Please, I also need the codes and datasets for this course. Thank you very much
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Could someone provide a step-by-step guide for a frontend developer looking to transition into a career in AI and machine learning? Any advice on the best steps to take would be greatly appreciated. Key Points to Achieve: 1. Develop a structured learning path for transitioning from frontend development to machine learning. 2. Gain essential skills in AI and machine learning. 3. Understand the prerequisites (e.g., foundational math and programming skills). 4. Identify relevant machine learning certifications or courses. 5. Acquire hands-on experience with ML projects to build a portfolio. 6. My goal is to make this career shift to facilitate moving abroad.
My question is regarding SVM, What if even after increasing dimensions (or features ), we are not getting any hyperplane which can divide our classes, in that situation what SVM provides ?
Everything is perfect, what a nice explanation, salute you guys. Only thing is as soon as Unsupervised learning starts, the alignment of video and the slide is poorly managed. It speaks slower and the slides move ahead. So it becomes very difficult as what you are speaking and what is shown in the slide does not match. Please correct this as I am not able to get anything after unsupervised learning. Rest all is perfectly explained.
This is a great tutorial that covers all areas of machine learning perfectly! Thanks to Edureka!! Can I please get the datasets and the codes used in the video?
Hey there :) We are glad to have you with us :) and more glad to have learners like you with us ! We are happy to share the source code ,please do drop your mail id to share the same :) Thank You for being a part of our Edureka team : ) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Hi Op, my question is regarding the challenge of dealing with datasets in my project . The feature set contains about 40 columns from merging10 csv files to train my model. But the test dataset contains just 2 columns including the target column. So each time I try to make classification on the target column I get a failure notice telling me that my training and test shape are not the same. How can I make my model to make prediction based on the test dataset given to me? Or how can I adapt my test dataset to conform to the training dataset in shape in order to run my program?
Thanks for the wonderful feedback! We are glad we could help. You can also check out our online training here: Post Graduate Program in Artificial Intelligence and Machine Learning - bit.ly/2YodPOK Machine Learning Engineer Masters Program - bit.ly/3bRGJL2 Machine Learning Certification Training using Python - bit.ly/2Yoa6Ra Use code "RUclips20" and enroll in our courses with Flat 20% off.
This is incredible. Thank you. The concepts are well presented in a logical order. The material is well thought and meticulously explained to help beginners. I hope your channel does very well because resources like this help all of us so much. The only recommendation I would make is to create shorter videos and put the whole course in a playlist. A 9+ hour video is very daunting for most people.
Thanks a lot for this course. Can you explain the relation between Inferential statistics and neural network models?? both can be used for predicting outcomes. Please do explain me in detail.
You are most welcome. In Inferential Statistics we have basic mathematical formulas and algorithm, but in neural networks we have deep learning which uses a combination of mathematical formula with the extra part of learning from the feedback and backpropagation.
Thank you so much : ) We are glad to be a part of your learning journey. Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thanks Edureka team for the course.But your "Artificial Intelligence full course"is very comprehensive introdution and very simple and understandable.This course is quite difficult to understand and very complicated.
I have the following versions: Python: 3.7.4 (default, Aug 9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)] scipy: 1.3.1 numpy: 1.17.3 matplotlib: 3.1.1 pandas: 0.25.1 sklearn: 0.21.3
Thanks for the wonderful feedback! We are glad we could help. You can also check out our complete training here: Post Graduate Program in Artificial Intelligence and Machine Learning - bit.ly/2YodPOK Machine Learning Engineer Masters Program - bit.ly/3bRGJL2 Machine Learning Certification Training using Python - bit.ly/2Yoa6Ra Use code "RUclips20" and enroll in our courses with Flat 20% off.
I am new to the world of Machine Learning, Thank you so much Edureka!! I have already clicked"like" and "subscribed" for this amazing channel. Can the datasets and the codes used by the instructors be shared? Thanks again!
We are very glad to hear that your a learning well with our contents :) continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
Thank you so much Edureka!! Can the datasets and the codes used by the instructors be shared? Also, it would be great to have some sort of notes of the tutorial if those can be shared as well. Thanks again!
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thanks so much edureka! for this amazing piece of intellectual resource. You guys are making complex stuffs look so easy. Can I get this PPT ? Thank you very much...😍
Hey there :) We are glad to have you with us :) and more glad to have learners like you with us ! We are happy to share the source code ,please do drop your mail id to share the same :) Thank You for being a part of our Edureka team : ) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thanks Edureka team for the course.. I'm new to ML and I want to use its algorithms for medical signals.. Does this course suitable for this purpose or I need another one... appreciate any help... thanks
Hi ! Good to know that our videos are helping you to learn better :) Please share your mail id to share the data sheets :)We’ll update you soon . Do subscribe the channel for more updates : )
Thanks for the compliment! Please share your email id here in the comment section (it will not be published). We will forward the code and dataset to your email address.
This tutorial is so well curated!! Thank you so much Edureka!! Can the datasets and the codes used by the instructors be shared with us? Also, it would be great to have some sort of notes of the tutorial if those can be shared as well. Thanks again!
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For Edureka Machine Learning & AI Masters Course Curriculum, Visit our Website: bit.ly/2QixjBC (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") Here is the video timeline: 2:47 What is Machine Learning?
4:08 AI vs ML vs Deep Learning
5:43 How does Machine Learning works?
6:18 Types of Machine Learning
6:43 Supervised Learning
8:38 Supervised Learning Examples
11:49 Unsupervised Learning
13:54 Unsupervised Learning Examples
16:09 Reinforcement Learning
18:39 Reinforcement Learning Examples
19:34 AI vs Machine Learning vs Deep Learning
22:09 Examples of AI
23:39 Examples of Machine Learning
25:04 What is Deep Learning?
25:54 Example of Deep Learning
27:29 Machine Learning vs Deep Learning
33:49 Jupyter Notebook Tutorial
34:49 Installation
50:24 Machine Learning Tutorial
51:04 Classification Algorithm
51:39 Anomaly Detection Algorithm
52:14 Clustering Algorithm
53:34 Regression Algorithm
54:14 Demo: Iris Dataset
1:12:11 Stats & Probability for Machine Learning
1:16:16 Categories of Data
1:16:36 Qualitative Data
1:17:51 Quantitative Data
1:20:55 What is Statistics?
1:23:25 Statistics Terminologies
1:24:30 Sampling Techniques
1:27:15 Random Sampling
1:28:05 Systematic Sampling
1:28:35 Stratified Sampling
1:29:35 Types of Statistics
1:32:21 Descriptive Statistics
1:37:36 Measures of Spread
1:44:01 Information Gain & Entropy
1:56:08 Confusion Matrix
2:00:53 Probability
2:03:19 Probability Terminologies
2:04:55 Types of Events
2:05:35 Probability of Distribution
2:10:45 Types of Probability
2:11:10 Marginal Probability
2:11:40 Joint Probability
2:12:35 Conditional Probability
2:13:30 Use-Case
2:17:25 Bayes Theorem
2:23:40 Inferential Statistics
2:24:00 Point Estimation
2:26:50 Interval Estimate
2:30:10 Margin of Error
2:34:20 Hypothesis Testing
2:41:25 Supervised Learning Algorithms
2:42:40 Regression
2:44:05 Linear vs Logistic Regression
2:49:55 Understanding Linear Regression Algorithm
3:11:10 Logistic Regression Curve
3:18:34 Titanic Data Analysis
3:58:39 Decision Tree
3:58:59 what is Classification?
4:01:24 Types of Classification
4:08:35 Decision Tree
4:14:20 Decision Tree Terminologies
4:18:05 Entropy
4:44:05 Credit Risk Detection Use-case
4:51:45 Random Forest
5:00:40 Random Forest Use-Cases
5:04:29 Random Forest Algorithm
5:16:44 KNN Algorithm
5:20:09 KNN Algorithm Working
5:27:24 KNN Demo
5:35:05 Naive Bayes
5:40:55 Naive Bayes Working
5:44:25Industrial Use of Naive Bayes
5:50:25 Types of Naive Bayes
5:51:25 Steps involved in Naive Bayes
5:52:05 PIMA Diabetic Test Use Case
6:04:55 Support Vector Machine
6:10:20 Non-Linear SVM
6:12:05 SVM Use-case
6:13:30 k Means Clustering & Association Rule Mining
6:16:33 Types of Clustering
6:17:34 K-Means Clustering
6:17:59 K-Means Working
6:21:54 Pros & Cons of K-Means Clustering
6:23:44 K-Means Demo
6:28:44 Hirechial Clustering
6:31:14 Association Rule Mining
6:34:04 Apriori Algorithm
6:39:19 Apriori Algorithm Demo
6:43:29 Reinforcement Learning
6:46:39 Reinforcement Learning: Counter-Strike Example
6:53:59 Markov's Decision Process
6:58:04 Q-Learning
7:02:39 The Bellman Equation
7:12:14 Transitioning to Q-Learning
7:17:29 Implementing Q-Learning
7:23:33 Machine Learning Projects
7:38:53 Who is a ML Engineer?
7:39:28 ML Engineer Job Trends
7:40:43 ML Engineer Salary Trends
7:42:33 ML Engineer Skills
7:44:08 ML Engineer Job Description
7:45:53 ML Engineer Resume
7:54:48 Machine Learning Interview Questions
I am clear with AI/ML/DL now but where will the data science role fit in these?
Please provide us the dataset
Thank you for the video. Where can I get the source code?
can you provide the github code used in the course
Please mention your email id (it will not be published). We will forward the code and dataset to your email address.
00:00 Introduction
2:47 What is Machine Learning?
4:08 AI vs ML vs Deep Learning
5:43 How does Machine Learning works?
6:18 Types of Machine Learning
6:43 Supervised Learning
8:38 Supervised Learning Examples
11:49 Unsupervised Learning
13:54 Unsupervised Learning Examples
16:09 Reinforcement Learning
18:39 Reinforcement Learning Examples
19:34 AI vs Machine Learning vs Deep Learning
22:09 Examples of AI
23:39 Examples of Machine Learning
25:04 What is Deep Learning?
25:54 Example of Deep Learning
27:29 Machine Learning vs Deep Learning
33:49 Jupyter Notebook Tutorial
34:49 Installation
50:24 Machine Learning Tutorial
51:04 Classification Algorithm
51:39 Anomaly Detection Algorithm
52:14 Clustering Algorithm
53:34 Regression Algorithm
54:14 Demo: Iris Dataset
1:12:11 Stats & Probability for Machine Learning
1:16:16 Categories of Data
1:16:36 Qualitative Data
1:17:51 Quantitative Data
1:20:55 What is Statistics?
1:23:25 Statistics Terminologies
1:24:30 Sampling Techniques
1:27:15 Random Sampling
1:28:05 Systematic Sampling
1:28:35 Stratified Sampling
1:29:35 Types of Statistics
1:32:21 Descriptive Statistics
1:37:36 Measures of Spread
1:44:01 Information Gain & Entropy
1:56:08 Confusion Matrix
2:00:53 Probability
2:03:19 Probability Terminologies
2:04:55 Types of Events
2:05:35 Probability of Distribution
2:10:45 Types of Probability
2:11:10 Marginal Probability
2:11:40 Joint Probability
2:12:35 Conditional Probability
2:13:30 Use-Case
2:17:25 Bayes Theorem
2:23:40 Inferential Statistics
2:24:00 Point Estimation
2:26:50 Interval Estimate
2:30:10 Margin of Error
2:34:20 Hypothesis Testing
2:41:25 Supervised Learning Algorithms
2:42:40 Regression
2:44:05 Linear vs Logistic Regression
2:49:55 Understanding Linear Regression Algorithm
3:11:10 Logistic Regression Curve
3:18:34 Titanic Data Analysis
3:58:39 Decision Tree
3:58:59 what is Classification?
4:01:24 Types of Classification
4:08:35 Decision Tree
4:14:20 Decision Tree Terminologies
4:18:05 Entropy
4:44:05 Credit Risk Detection Use-case
4:51:45 Random Forest
5:00:40 Random Forest Use-Cases
5:04:29 Random Forest Algorithm
5:16:44 KNN Algorithm
5:20:09 KNN Algorithm Working
5:27:24 KNN Demo
5:35:05 Naive Bayes
5:40:55 Naive Bayes Working
5:44:25Industrial Use of Naive Bayes
5:50:25 Types of Naive Bayes
5:51:25 Steps involved in Naive Bayes
5:52:05 PIMA Diabetic Test Use Case
6:04:55 Support Vector Machine
6:10:20 Non-Linear SVM
6:12:05 SVM Use-case
6:13:30 k Means Clustering & Association Rule Mining
6:16:33 Types of Clustering
6:17:34 K-Means Clustering
6:17:59 K-Means Working
6:21:54 Pros & Cons of K-Means Clustering
6:23:44 K-Means Demo
6:28:44 Hierarchical Clustering
6:31:14 Association Rule Mining
6:34:04 Apriori Algorithm
6:39:19 Apriori Algorithm Demo
6:43:29 Reinforcement Learning
6:46:39 Reinforcement Learning: Counter-Strike Example
6:53:59 Markov's Decision Process
6:58:04 Q-Learning
7:02:39 The Bellman Equation
7:12:14 Transitioning to Q-Learning
7:17:29 Implementing Q-Learning
7:23:33 Machine Learning Projects
7:38:53 Who is a ML Engineer?
7:39:28 ML Engineer Job Trends
7:40:43 ML Engineer Salary Trends
7:42:33 ML Engineer Skills
7:44:08 ML Engineer Job Description
7:45:53 ML Engineer Resume
7:54:48 Machine Learning Interview Questions
2 semesters worth of classes in one vid. Thanks
The best training I had seen till now on youtube, no adds, no waste of time, very detail!
this is literally a piece of god, 10 hr video and not a single add. God bless you
Successfully completed 9 hour 40 minutes course in 3 days 😧. Thanks a lot😘
This channel is doing more good than government.
Oh my god! I wanted this thing so badly.
Thanks .Love from 🇱🇰 SriLanka.
This is Amazing video. I am a PhD students in CS and I feel I benefited from this tutorial more than all of m study :)
I'm up to 5 hours into the video so far.. excellent teaching, looking forward to the last parts..thank you.
Wow . This is is one of the best ML youtube courses i have ever attended. Its a job well done , its informative , it teaches you everything u wanna know about ML in one short. Thanks guys a job well done!
Thanks for the compliment! We are glad you loved the video. Do subscribe to the channel and hit the bell icon to never miss an update from us in the future. Cheers!
You know the video will be great when you see an indian guy in it
Thanks a lot! It's really a treasurer for all in programming.
This lecture got the best explanation throughout the RUclips , i would like to thank to #edureka! and #RUclips for this amazing work
Thanks for the compliment. Do subscribe, like and share to stay connected with us. Cheers!
@@edurekaIN yeah true
Dear Edureka! This is one of the finest videos I have seen that has really helped me prepare well for my campus placement process
Hey, thanks for the compliment! We are glad we could help. Do subscribe to our channel to stay posted on upcoming tutorials.
machine learning has been my nightmare for yrs. Not anymore ; All thanks to the faculty invlolved in this video and Edureka!!!!!!!
Ohh! Thanks a lot.
Finally i got this.
This is awesome. Help me understand ML and helped me with my coursework.
Literally thanks from my bottom of my 💓 heart
edureka team thanks a ton for taking these sections that too for free..
Thanks .Love from 🇱🇰 SriLanka.
Please make more courses on NLP : Crash Course, Interview Questions, Resume building for NLP freshers.
Really resourceful. This course helped get up to speed on the subject. Thank you Edureka
The girl who taught logistic regression was teaching so good and I understood everything...💜💜
I like this course..
This is the best course available on RUclips.❤️❤️☺️
I have completed python for beginner 7hr course and data science and nowthis is 3rd best course thanks edureka
I must recommend every user to watch python for beginner
Thanks a lot Noorbano 🙏
Literally getting goosebumps after listening to words "MACHINE LEARNING". "ANACONDA" ..i will watch after NIELIT O level Exam next month
This is the best I've ever learn on machine learning
I greatly want to thank you for making this video as it incorporates the perfect necessities to learn about ML and automation. I am currently trying to implement this for my app and it has been a real lifesaver.
One of the most clear explanations about Machine Learning on internet. Thanks for the course , it really helped me in my Data science career. Only one request can you please share the datasheet and the code?
Hi great to hear from you :) please share your mail id ! so that we can share the data sheet with you :)Do subscribe the channel for more updates : )
Thanks a lot from deep of my heart. I am waiting this course for a long time !!!
You are most welcome. The wait is over now. Happy Learning!
I think to add subtitles is easier to learn more
and also thanks to edureka gives this valuable tutorial for ours.
Hi Mohammad, thanks for watching the video. We will definitely look into your suggestions. Do subscribe to our channel and stay connected with us. Cheers!
Thanks for the awesome tutorial, very informative.
When I run the code in "54:14 Demo: Iris Dataset ", everything went well until the part that i was supposed to run the algorithms and test the accuracy. The code gave me error. Kindly find below the code, which part is wrong?
# Evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
When I run the above code, it gives this error
ValueError: Setting a random_state has no effect since shuffle is False. You should leave random_state to its default (None), or set shuffle=True.
Among all lecturer , don't know Her name but
Logistic regression was prepared best and awesome explanation as well thank you 😍😍💗
Best tutorial video i've seen on RUclips. I am a complete beginner in ML and this has really given me a great insight on ML. Thank you so much
Hi : ) We really are glad to hear this ! Truly feels good that our team is delivering and making your learning easier :) Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Really gud everythg in single video d way its shown thanx Atul
I just started the course and will definitely finish it. Thanks for the video needed it so badly.
How many of you finished the course?
@edureka! Thanks so much for this amazing piece of intellectual resource. You guys are making complex stuffs look so easy. Please, I also need the codes and datasets for this course. Thank you very much
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
These courses are the best....😍😍😍😍
Good day of study starting from the edureka channel.thanks By Hind Association Team.(izak)
Whole video contains 0 adds.
This type of work only done by people's who has a great heart.
Thank you
Explanation at it's best...love you 3000
Could someone provide a step-by-step guide for a frontend developer looking to transition into a career in AI and machine learning? Any advice on the best steps to take would be greatly appreciated.
Key Points to Achieve:
1. Develop a structured learning path for transitioning from frontend development to machine learning.
2. Gain essential skills in AI and machine learning.
3. Understand the prerequisites (e.g., foundational math and programming skills).
4. Identify relevant machine learning certifications or courses.
5. Acquire hands-on experience with ML projects to build a portfolio.
6. My goal is to make this career shift to facilitate moving abroad.
Adept course! Highly recommend it! Thanks, Edureka team!
You are welcome👍
Do subscribe to our channel to stay posted on upcoming tutorials: ruclips.net/user/edurekaIN.
My question is regarding SVM,
What if even after increasing dimensions (or features ), we are not getting any hyperplane which can divide our classes, in that situation what SVM provides ?
searching for this from 2 days all over youtube!!
Everything is perfect, what a nice explanation, salute you guys. Only thing is as soon as Unsupervised learning starts, the alignment of video and the slide is poorly managed. It speaks slower and the slides move ahead. So it becomes very difficult as what you are speaking and what is shown in the slide does not match. Please correct this as I am not able to get anything after unsupervised learning. Rest all is perfectly explained.
time stamps:
👇🏼👇🏼👇🏼
6:18 Types of Machine Learning
6:43 Supervised Learning
8:38 Supervised Learning Examples
11:49 Unsupervised Learning
13:54 Unsupervised Learning Examples
16:09 Reinforcement Learning
18:39 Reinforcement Learning Examples
Very very very very usefull Thank you
This is a great tutorial that covers all areas of machine learning perfectly! Thanks to Edureka!! Can I please get the datasets and the codes used in the video?
Hey there :) We are glad to have you with us :) and more glad to have learners like you with us ! We are happy to share the source code ,please do drop your mail id to share the same :) Thank You for being a part of our Edureka team : ) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
33:50 writning down this comment so that i can resume video from here next time
going great a few drawbacks but really gave me a brief overview of what machine learning is in depth !!!
Thanks a lot for this video.......
Do we need to have basic understanding of MATPLOTLIB,PANDAS,NUMPY for ML Engineer ?
thanks !!!!!!!!!!! this is really the best tutorial.
You are welcome👍
Do subscribe to our channel to stay posted on upcoming tutorials: ruclips.net/user/edurekaIN.
Hi Op, my question is regarding the challenge of dealing with datasets in my project . The feature set contains about 40 columns from merging10 csv files to train my model. But the test dataset contains just 2 columns including the target column. So each time I try to make classification on the target column I get a failure notice telling me that my training and test shape are not the same.
How can I make my model to make prediction based on the test dataset given to me? Or how can I adapt my test dataset to conform to the training dataset in shape in order to run my program?
awesomely explained Entropy and Information Gain... Thumbs Up.
Hi Ketan, we are glad you loved the video. Do subscribe to our channel to stay connected with us. Cheers!
One of the best channel I have ever seen 😍.. The best part is u are teaching from the basics.. Anyone can understand without any difficulty ☺
Thanks for the wonderful feedback! We are glad we could help.
You can also check out our online training here:
Post Graduate Program in Artificial Intelligence and Machine Learning - bit.ly/2YodPOK
Machine Learning Engineer Masters Program - bit.ly/3bRGJL2
Machine Learning Certification Training using Python - bit.ly/2Yoa6Ra
Use code "RUclips20" and enroll in our courses with Flat 20% off.
This is incredible. Thank you. The concepts are well presented in a logical order. The material is well thought and meticulously explained to help beginners. I hope your channel does very well because resources like this help all of us so much.
The only recommendation I would make is to create shorter videos and put the whole course in a playlist. A 9+ hour video is very daunting for most people.
Hi Sakaba, check out this playlist: ruclips.net/p/PL9ooVrP1hQOHUfd-g8GUpKI3hHOwM_9Dn
Hello, your courses are very helpful. Thank you.
Good one.... Lots of stuff covered... Explanation is good... Thanks
I was waiting for this video for a long time and finally found it
Thanks a lot team #edureka
Congratulations for 1 Million subscribers!!✌️✌️
Thanks a lot 🙏
Best video on ML i have seen so far ! please share the notes and the datasets
Thanks a lot for this course. Can you explain the relation between Inferential statistics and neural network models?? both can be used for predicting outcomes. Please do explain me in detail.
You are most welcome. In Inferential Statistics we have basic mathematical formulas and algorithm, but in neural networks we have deep learning which uses a combination of mathematical formula with the extra part of learning from the feedback and backpropagation.
nice explantion ......👏 thanku you sir and go head like this with many more videos that helps for learner's....😊
Thank you so much : ) We are glad to be a part of your learning journey. Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thanks Edureka team for the course.But your "Artificial Intelligence full course"is very comprehensive introdution and very simple and understandable.This course is quite difficult to understand and very complicated.
you are giving many paid course for free thanks atul sir and edureka
I have the following versions:
Python: 3.7.4 (default, Aug 9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]
scipy: 1.3.1
numpy: 1.17.3
matplotlib: 3.1.1
pandas: 0.25.1
sklearn: 0.21.3
Finally something extremely wonderful in my RUclips search history.
Thank u edureka, its the course that i eagerly wanted..love from nepal
You are welcome Nitesh. Don't forget to Subscribe us.
Me2 from Nepal
Helpful in learning ML...thanku so much Edureka
Waiting for this course long time
The wait is over, Happy Learning!
You just got a new subscriber
Thanks, Edureka.❤❤❤❤❤
Love from Heart💕💕
✔😍👌
this s an awesome awesome course, well explained, i would like to rate it 4.8 out of 5
Thanks for the wonderful feedback! We are glad we could help.
You can also check out our complete training here:
Post Graduate Program in Artificial Intelligence and Machine Learning - bit.ly/2YodPOK
Machine Learning Engineer Masters Program - bit.ly/3bRGJL2
Machine Learning Certification Training using Python - bit.ly/2Yoa6Ra
Use code "RUclips20" and enroll in our courses with Flat 20% off.
Oh my god! I wanted this thing so badly.
Now its here 🙂 Happy Learning!
I am new to the world of Machine Learning, Thank you so much Edureka!!
I have already clicked"like" and "subscribed" for this amazing channel.
Can the datasets and the codes used by the instructors be shared?
Thanks again!
Hi please share your mail id to share the data sheet :) We'll Update you soon ! Do Subscribe the channel to keep updated
Thank you !!!
WoW ! Well structured course for ML. Thanks a lot. Can you please provide the datasets and the codes as well, it would be really helpful.
We are very glad to hear that your a learning well with our contents :) continue to learn with us and don't forget to subscribe our channel so that you don't miss any updates !
Thank you
This is extraordinary effort by your team although i just start watching your video. It is just adorable
Sir, you are great and we are blessed to have you ❤️🙏😇
just start the course, very excited for it
You deserve a big heart
Thanks a lot
Thank you so much Edureka!! Can the datasets and the codes used by the instructors be shared? Also, it would be great to have some sort of notes of the tutorial if those can be shared as well. Thanks again!
Good to know our contents and videos are helping you learn better . We are glad to have you with us ! Please share your mail id to send the data sheets to help you learn better :) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
Thank you very much for sharing.
Absolutely well done and definitely keep it up!!! 👍👍👍👍👍
Thanks for sharing this video. This is awesome. You help a lot
Thanks so much edureka!
for this amazing piece of intellectual resource. You guys are making complex stuffs look so easy. Can I get this PPT ? Thank you very much...😍
Hey there :) We are glad to have you with us :) and more glad to have learners like you with us ! We are happy to share the source code ,please do drop your mail id to share the same :) Thank You for being a part of our Edureka team : ) Do subscribe the channel for more updates : ) Hit the bell icon to never miss an update from our channel : )
This course is DOPE !!
5 stars
too good this lecture is........
Thanks edureka waiting for this video... Pls make a video on EDA in ML... Congratulations for 1 million subscribers...
You are most welcome, Pooja. We will process your request as soon as possible. Do subscribe to our channel and stay connected with us. Cheers :)
@@edurekaIN Already subscribed... Thanks for reply...
🙏
Thank you so much I owe tons ❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️
Thanks Edureka team for the course.. I'm new to ML and I want to use its algorithms for medical signals.. Does this course suitable for this purpose or I need another one... appreciate any help... thanks
Thanks! Yes, if you want to get certified in machine learning, then you can refer to the link: www.edureka.co/machine-learning-certification-training
Wow! Best video on ML i've seen so far. Can you please provide the datasets and the codes as well.
Hi ! Good to know that our videos are helping you to learn better :) Please share your mail id to share the data sheets :)We’ll update you soon . Do subscribe the channel for more updates : )
Thanks, Edureka for this tutorial. It was really good and informative
Can you please provide the datasets and code
Thanks for the compliment! Please share your email id here in the comment section (it will not be published). We will forward the code and dataset to your email address.
This tutorial is so well curated!! Thank you so much Edureka!! Can the datasets and the codes used by the instructors be shared with us? Also, it would be great to have some sort of notes of the tutorial if those can be shared as well. Thanks again!
Thanks for the compliment! Please share your email id with us (it will not be published). We will forward the code and dataset to your email address.
Best video for Machine Learning 👍💯
Glad you think so!! We are glad to have learners like you . Do subscribe our channel and hit that bell icon to never miss an video from our channel :)