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))
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.
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.
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).
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
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 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 ❤
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.
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.
Great explanation! The breakdown of different machine learning algorithms was super helpful. Which algorithm do you find works best for most real-world problems you’ve encountered?
As someone that has 0 experience with algorithms and it’s learning python now. This was very hard to follow, the only time when it was easy for me to keep up was where you gave practical examples. you gave out a lot of information in a really short time so maybe do 1 video for each algorithm explained for dummies like myself??
Hellooo..... ur explntn nd wat sub topics comes under whch topic is explnd very well ....... thnkww so muchHhh .... can uhHh plzZzz alsoOo mention where to use maths topic here lyKk calculs etc ... lykKk were do we requrd to use dem nd wen ... jst d topics .... so dat we get an idea wat maths all topics will comes under whch algo..
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.
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.
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.
Your video makes me want to run to the library right away. No shit. Last semester I wasn't satisfied with the grade that I got in stat's. I thought I like math but not stats, but maybe that isn't true.
![The moment we stopped understanding AI [AlexNet]](http://i.ytimg.com/vi/UZDiGooFs54/mqdefault.jpg)
![The moment we stopped understanding AI [AlexNet]](/img/tr.png)
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
@@mrawesome7739 Python is only used because it has a lot of libraries that do all of the complicated things for you
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
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.
He just showed PCA lol
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.
Just took my 5 month long intro to ML course in 17 minutes! Nice.
Please please more computer science content like this!!! ❤️
Thank you, I have an final exam in about 14 hours and needed a good refresher on the material!
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
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).
Wow! I've taken many machine learning courses to date, but his breakdown is spot on! So concise! 🎉👍 Great job. Do you have more?!
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
This is one of my favorite videos on youtube
Studying for my midterm next week. This was a great quick overview!
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!!!
bro best video on youtube so far, thank you so much for this video.
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 is great video! Nice and clear. Thank you. 👏🏻
Great explanation! Time to dive into them one by one
Finally, an amazing video that is not clickbait
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 ❤
I do not know whether this is a person or not. This is the best explanation.
Now this...this is good content. Keep it up. You earned a subscriber!
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.
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.
The best video among others on the subject I've been passing through. Thank you
Man, I love this video. Thank you so much for this video, now I'm confident about learning machine learning.
My whole semester material in one video. I love it!!! 🎉
Loved it. Quick and easy to understand. ❤
I recommend this video. Not only a time saver, but quite a good description of what these methods do and when they work best 🎉
Thank you for this well explained video
Every second of this video is beyond the scope of this video 😅
Which helps to make it concise. You get to choose what you want to expand on.
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.
Really well done - thanks for sharing.
these short visualized explanations help way more than a certain online course im currently taking 😭👍
What course are you taking?
Great explanation! The breakdown of different machine learning algorithms was super helpful. Which algorithm do you find works best for most real-world problems you’ve encountered?
As someone that has 0 experience with algorithms and it’s learning python now. This was very hard to follow, the only time when it was easy for me to keep up was where you gave practical examples. you gave out a lot of information in a really short time so maybe do 1 video for each algorithm explained for dummies like myself??
Pause the video and make notes until you understand.
Wonderful, waiting for more content like this
This man explain exceptionally
thank you very much well explained
Hellooo..... ur explntn nd wat sub topics comes under whch topic is explnd very well ....... thnkww so muchHhh .... can uhHh plzZzz alsoOo mention where to use maths topic here lyKk calculs etc ... lykKk were do we requrd to use dem nd wen ... jst d topics .... so dat we get an idea wat maths all topics will comes under whch algo..
Wonderful video. Thank you so much for taking the time to create this.
this is a great summary for ML learners
Thank you; it was super helpful for me to understand the big picture of ML!
Awesome sir. Many thanks. - Nepali from USA
This is an amazing introduction!!
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.
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
This is really amazing thank you so much
Cooles Video! Danke dir, btw dein english ist sehr gut
Great explained and good to remember some algorithms in the future
Wonderful, Nice video! 10 years in business.
What do you consider is the best paying skill in a Data Scientist? 😊.
top video! make a part 2 with more advanced algorithms like sarimax etc
this video is great and deserves the thumbs up
Best short-course! However, do you have the same for REinforcement learning, specifically inverse, and how constraint satisfaction associates with ML?
Excellent overview
It's very interesting and easy to understand, we need real time example with code in seperate topics
Sir, this was soo helpful and easy to understand. Thanks a lot for sharing
Excellent video!, Thank you!
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.
I will share your channel as mush as I can
Terrific video on ML within 16 mintues
This is so helpful omg
interesting example at 8:15
😭
a gem in youtube
Good lesson.
thank you very much!
Yeah, man. You know what? You're some sort of Didactics Super Sayan.
Thanks for the video. Instant subscribe.
Great video, thanks
Please Please make moew videos on machine learning
I find this as the best resource to learn all the concepts
This was awesome!
Great Video🙏👌
Thank you so much for this! easy to understand 👍
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 video, thanks for making this. At the end however, I’m unable to see the last two slides due to cards covering it.
TH-cam recommend this channel as No Fluff channel,
great content. thank you so much!
Amazing!!
Nice explanation, Thank you!
was a very great video thanks
Awesome, thanks!!
Very helpful, thanks 🙂
Man you did a great jon
Awesome video, thanks
8:15 great example
never understood how machine learning works till now
Awesome video!
It's so clear
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
What algorithm do you use when the features are tokens and the predicted object is a category?
great explanation
Your video makes me want to run to the library right away. No shit. Last semester I wasn't satisfied with the grade that I got in stat's. I thought I like math but not stats, but maybe that isn't true.
I feel you. I used to think the same now I really like stats. Also check out my buddy about math for ml
Great video, but I think you left out one important unsupervised learning, the Self-Organizing Map, (SOM)
All a.i concepts in 10 mins plz .like iceberg
Which algorithm is incremental and continuous?
Great Job bro
Great content.
Can we have similar video for Deep learning as well.
this was so overwhelming