I'm starting to get in to it but I think you need to have some background in data science, math (not 100% required, but linear algebra is most important, but also Calculus is useful), and coding (Python is probably the best for machine learning, (which is computationally slower than things like C, but built for data science))
perhaps the week you spent trying to learn these things by yourself, contributed to you understanding the video, so it probably wasn't a total waste. But to be sure, you should do some "unsupervised learning" experiments to verify your results.
00:03 Overview of major machine learning algorithms 01:52 Supervised learning involves predicting and classifying data 03:41 Machine learning algorithms explained in 17 min 05:31 Understanding the importance of hyperparameters in machine learning algorithms 07:16 Kernel functions allow for the efficient creation of nonlinear decision boundaries. 09:07 Ensemble algorithms combine multiple decision trees to create powerful models. 10:50 Machine learning algorithms are designed to design complex features implicitly 12:34 Introduction to Unsupervised Learning and Clustering 14:21 Dimensionality Reduction in Machine Learning 16:10 Common machine learning algorithms explained
I'm preparing a class about algorithms for high school students and this video has synthesized and simplified like half of the job. I'll have to give you credit in the references section. Great job!
You just covered the first month of my 400 level machine learning class, minus the math, examples, and a a couple newer dimension reduction techniques. This video is a good resource.
As someone who knows and uses all algorithms, I will store this video to rewatch from times to times, just to not forget about solutions, I might not using that much. Well explained for a brief overview.
This was extremely useful, thank you. If anyone is learning deep learning, this is a great place to start. Every deep learning book/course should open with an overview of the algorithms (would have saved me a lot of confusion).
This is just awesome, I was trying to learn ml models since 2-3 months but getting confused, this one video made me understand each with clarity in just 1 hour😮, this is awesome ❤
Thanks for this guideline. It makes me want to actually take a class on data science and information theory. I've been putting off learning about them for so long since I figured I could just theorized them from fundamental by tracing math knowledge. But, reality is that I can only do so much to reinvent the wheel when I don't know the existing wheels out there.
Great content! But I'd love to see a series of videos exploring each of these algorithms step by step, with real life examples and with proper time for understanding it. Throwing it all at once is hard to follow.
Hi, thank you for you channel, really helpful - I wanna ask you to make a video about Reinforcement Learning. About the algorithm per se, the state of the algorithm in ML industry and where it is using in current moment. Thank you.
I would add the distinction between relationship based and data driven models. Relationship-based statistical models rely on predefined hypotheses and the relationships between variables. Once a hypothesis is confirmed, additional data is not necessary for validation. On the other hand, data-driven machine learning models continuously learn and improve from the data, identifying patterns without the need for predefined hypotheses.
Love the animations and simplicity you explained all the topics. Could you take more time to upload more such videos but with complete lectures on each topic. Everybody will love that.
very unique and my one of the faviorite video cause im looking for it for a long time and please make video on how to select regression models and classfication models like some patternor or trick to choose appropriate models thank you good video
Very great ! Would be also very interesting to combine it with type of deep learning models, like CNN, RNN, encoder/decoder, LMM etc, and in which use case you use them with limitations :) , I think I haven't seen yet these kind of deep learning overwiew.
Don’t forget to like and subscribe!* *Doing so requires you to locate and navigate to the “like” and “subscribe” buttons respectively, which is (literally) beyond the scope of this video.
i have a question what type of algo will be used if someone wants to create a model that helps marts(as Walmart type of stores) to predict what type of product should they buy more using historical data of the store, notify the management of the stocks that are low. also in this type of problem should they use both classification and regression algo?
At 16:29 you're describing the regression line but isn't there a typo within the sum of the squared residuals showing a y-bar instead of y-hat? The description then correctly calls the y-hat "average of dependent variables", but then doesn't that make the equation the total sum of squares and not the sum of squared residuals?
Amazing! As someone who wants to learn ML but has little to no idea about it yet, this video was really easy to follow. Keep it up!
I'm starting to get in to it but I think you need to have some background in data science, math (not 100% required, but linear algebra is most important, but also Calculus is useful), and coding (Python is probably the best for machine learning, (which is computationally slower than things like C, but built for data science))
My name is karthik i have the same feeling
Love and respect from a small village in India i even can't have this type of valuable info from the paid sources Thanks you so much🥰
Keep it up king! You got this!
Any idea where I can learn all stats and regression shown in the video@@bananasmileclub5528
Dude WHAT? I spent a week trying to understand all of these and here I am, understood everything crystal clear in an hour 🤨
perhaps the week you spent trying to learn these things by yourself, contributed to you understanding the video, so it probably wasn't a total waste. But to be sure, you should do some "unsupervised learning" experiments to verify your results.
00:03 Overview of major machine learning algorithms
01:52 Supervised learning involves predicting and classifying data
03:41 Machine learning algorithms explained in 17 min
05:31 Understanding the importance of hyperparameters in machine learning algorithms
07:16 Kernel functions allow for the efficient creation of nonlinear decision boundaries.
09:07 Ensemble algorithms combine multiple decision trees to create powerful models.
10:50 Machine learning algorithms are designed to design complex features implicitly
12:34 Introduction to Unsupervised Learning and Clustering
14:21 Dimensionality Reduction in Machine Learning
16:10 Common machine learning algorithms explained
Thank you, I have an final exam in about 14 hours and needed a good refresher on the material!
I'm preparing a class about algorithms for high school students and this video has synthesized and simplified like half of the job. I'll have to give you credit in the references section. Great job!
Just took my 5 month long intro to ML course in 17 minutes! Nice.
I am happy to realize that I already used all of those and played with the implementation of half of them in the doctorate.
Learning Machine Learning is amazing with this video
You just covered the first month of my 400 level machine learning class, minus the math, examples, and a a couple newer dimension reduction techniques. This video is a good resource.
He just showed PCA lol
I do not know whether this is a person or not. This is the best explanation.
Thank you for this well explained video
Great explanation! Time to dive into them one by one
Every second of this video is beyond the scope of this video 😅
Dude, you just made my concepts so clear in just 17 minutes. Now I know what to use for my application. Thank you very much! You are Amazing!!!
As someone who knows and uses all algorithms, I will store this video to rewatch from times to times, just to not forget about solutions, I might not using that much. Well explained for a brief overview.
Finally, an amazing video that is not clickbait
Studying for my midterm next week. This was a great quick overview!
This man explain exceptionally
bro best video on youtube so far, thank you so much for this video.
Please please more computer science content like this!!! ❤️
thank you very much well explained
This was extremely useful, thank you. If anyone is learning deep learning, this is a great place to start. Every deep learning book/course should open with an overview of the algorithms (would have saved me a lot of confusion).
Really well done - thanks for sharing.
Wow! I've taken many machine learning courses to date, but his breakdown is spot on! So concise! 🎉👍 Great job. Do you have more?!
My whole semester material in one video. I love it!!! 🎉
8:15 great example
this is a great summary for ML learners
This is just awesome, I was trying to learn ml models since 2-3 months but getting confused, this one video made me understand each with clarity in just 1 hour😮, this is awesome ❤
The best video among others on the subject I've been passing through. Thank you
Excellent overview
Thanks for this guideline. It makes me want to actually take a class on data science and information theory.
I've been putting off learning about them for so long since I figured I could just theorized them from fundamental by tracing math knowledge.
But, reality is that I can only do so much to reinvent the wheel when I don't know the existing wheels out there.
Wonderful, waiting for more content like this
This is an amazing introduction!!
I recommend this video. Not only a time saver, but quite a good description of what these methods do and when they work best 🎉
Great content! But I'd love to see a series of videos exploring each of these algorithms step by step, with real life examples and with proper time for understanding it. Throwing it all at once is hard to follow.
Hi, thank you for you channel, really helpful - I wanna ask you to make a video about Reinforcement Learning. About the algorithm per se, the state of the algorithm in ML industry and where it is using in current moment. Thank you.
I would add the distinction between relationship based and data driven models.
Relationship-based statistical models rely on predefined hypotheses and the relationships between variables. Once a hypothesis is confirmed, additional data is not necessary for validation.
On the other hand, data-driven machine learning models continuously learn and improve from the data, identifying patterns without the need for predefined hypotheses.
Great explained and good to remember some algorithms in the future
Now this...this is good content. Keep it up. You earned a subscriber!
these short visualized explanations help way more than a certain online course im currently taking 😭👍
What course are you taking?
Love the animations and simplicity you explained all the topics.
Could you take more time to upload more such videos but with complete lectures on each topic. Everybody will love that.
Awesome sir. Many thanks. - Nepali from USA
Man, I love this video. Thank you so much for this video, now I'm confident about learning machine learning.
Great video, thanks
top video! make a part 2 with more advanced algorithms like sarimax etc
Please Please make moew videos on machine learning
I find this as the best resource to learn all the concepts
Wonderful video. Thank you so much for taking the time to create this.
Thank you; it was super helpful for me to understand the big picture of ML!
Excellent video!, Thank you!
Best short-course! However, do you have the same for REinforcement learning, specifically inverse, and how constraint satisfaction associates with ML?
This was awesome!
RUclips recommend this channel as No Fluff channel,
a gem in youtube
this video is great and deserves the thumbs up
It's very interesting and easy to understand, we need real time example with code in seperate topics
Yeah, man. You know what? You're some sort of Didactics Super Sayan.
Thanks for the video. Instant subscribe.
very unique and my one of the faviorite video cause im looking for it for a long time and please make video on how to select regression models and classfication models like some patternor or trick to choose appropriate models thank you good video
Man you did a great jon
Sir, this was soo helpful and easy to understand. Thanks a lot for sharing
What algorithm do you use when the features are tokens and the predicted object is a category?
Awesome video, thanks
great content. thank you so much!
Nice explanation, Thank you!
Great video, thanks for making this. At the end however, I’m unable to see the last two slides due to cards covering it.
In 12:56, it shows Association under unsupervised learning. How does that task differ from clustering and dimension reduction?
was a very great video thanks
Very helpful, thanks 🙂
great explanation
Very great ! Would be also very interesting to combine it with type of deep learning models, like CNN, RNN, encoder/decoder, LMM etc, and in which use case you use them with limitations :) , I think I haven't seen yet these kind of deep learning overwiew.
Awesome video!
never understood how machine learning works till now
Awesome, thanks!!
Thank you so much for this! easy to understand 👍
Wonderful, Nice video! 10 years in business.
What do you consider is the best paying skill in a Data Scientist? 😊.
Informative.
Great content.
It's so clear
Which algorithm is incremental and continuous?
pls make a more indepth video on this topic or realise a course on data science and machine learning we want to learn from you
Great Job bro
which one is most useful and better to learn for future?
Great video, but I think you left out one important unsupervised learning, the Self-Organizing Map, (SOM)
Very useful!
now I understood non-linearity
Don’t forget to like and subscribe!*
*Doing so requires you to locate and navigate to the “like” and “subscribe” buttons respectively, which is (literally) beyond the scope of this video.
All a.i concepts in 10 mins plz .like iceberg
Bro thought we wouldnt notice @8:13
Hehe yeah I had a giggle
it was a very relevant example thou!
i have a question what type of algo will be used if someone wants to create a model that helps marts(as Walmart type of stores) to predict what type of product should they buy more using historical data of the store, notify the management of the stocks that are low. also in this type of problem should they use both classification and regression algo?
this road map was great
At 16:29 you're describing the regression line but isn't there a typo within the sum of the squared residuals showing a y-bar instead of y-hat? The description then correctly calls the y-hat "average of dependent variables", but then doesn't that make the equation the total sum of squares and not the sum of squared residuals?
The time stamp is wrong so I'm not sure what you're referring to
@@InfiniteCodes_ Sorry! It's at 3:24
Yes you are correct! I copied the wrong equation, good catch!
I know some statisticians that would be triggered by you called these methods machine learning, but nice vid
Great 👍
Lookingror this type of vid
Great Video! An update with gaussian processes would be cool. They are non conventional, and not so famous, but part of the neighborhood 😅
You can always add extra to videos like this one, but this is good enough to give you a taster.
Now do the 10-part-14-hour-long videos on MatLAB, Python, and R
Instant subscribe on my part.
very interesting!
Thanks sir !
I use Optuna for selecting my algo.