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.
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 ❤
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).
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.
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
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.
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
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.
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 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?
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.
![The moment we stopped understanding AI [AlexNet]](http://i.ytimg.com/vi/UZDiGooFs54/mqdefault.jpg)
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))
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 🤨
Wow! I've taken many machine learning courses to date, but his breakdown is spot on! So concise! 🎉👍 Great job. Do you have more?!
Please please more computer science content like this!!! ❤️
Just took my 5 month long intro to ML course in 17 minutes! Nice.
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!
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
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!!!
Studying for my midterm next week. This was a great quick overview!
I am happy to realize that I already used all of those and played with the implementation of half of them in the doctorate.
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 ❤
My whole semester material in one video. I love it!!! 🎉
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 a great summary for ML learners
Awesome sir. Many thanks. - Nepali from USA
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.
Every second of this video is beyond the scope of this video 😅
Great explanation! Time to dive into them one by one
The best video among others on the subject I've been passing through. Thank you
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 recommend this video. Not only a time saver, but quite a good description of what these methods do and when they work best 🎉
Finally, an amazing video that is not clickbait
Thank you; it was super helpful for me to understand the big picture of ML!
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.
RUclips recommend this channel as No Fluff channel,
Excellent overview
Man, I love this video. Thank you so much for this video, now I'm confident about learning machine learning.
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.
It's very interesting and easy to understand, we need real time example with code in seperate topics
Great explained and good to remember some algorithms in the future
top video! make a part 2 with more advanced algorithms like sarimax etc
these short visualized explanations help way more than a certain online course im currently taking 😭👍
What course are you taking?
Wonderful video. Thank you so much for taking the time to create this.
Wonderful, Nice video! 10 years in business.
What do you consider is the best paying skill in a Data Scientist? 😊.
Excellent video!, Thank you!
Now this...this is good content. Keep it up. You earned a subscriber!
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
Wonderful, waiting for more content like this
Yeah, man. You know what? You're some sort of Didactics Super Sayan.
Thanks for the video. Instant subscribe.
This was awesome!
never understood how machine learning works till now
Sir, this was soo helpful and easy to understand. Thanks a lot for sharing
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.
this video is great and deserves the thumbs up
This is an amazing introduction!!
a gem in youtube
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.
All a.i concepts in 10 mins plz .like iceberg
It's so clear
Great video, thanks for making this. At the end however, I’m unable to see the last two slides due to cards covering it.
Man you did a great jon
8:15 great example
Very helpful, thanks 🙂
Awesome video, thanks
great explanation
Nice explanation, Thank you!
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 content. thank you so much!
Great video, but I think you left out one important unsupervised learning, the Self-Organizing Map, (SOM)
which one is most useful and better to learn for future?
Thank you so much for this! easy to understand 👍
Informative.
I know some statisticians that would be triggered by you called these methods machine learning, but nice vid
Awesome, thanks!!
now I understood non-linearity
was a very great video thanks
What algorithm do you use when the features are tokens and the predicted object is a category?
Great content.
Awesome video!
Now do the 10-part-14-hour-long videos on MatLAB, Python, and R
Very useful!
this road map was great
successfully observation to learn them
Thanks ❤
Great Job bro
Thanks sir !
Bro thought we wouldnt notice @8:13
Hehe yeah I had a giggle
it was a very relevant example thou!
Lookingror this type of vid
I use Optuna for selecting my algo.
Instant subscribe on my part.
thanks
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.
very interesting!
Great 👍
You are amazing
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?
Which algorithm is incremental and continuous?
Now I can mention in the resume, ML Expert 😅
we need more videos if we want to understand better how AI works
good video
Do you think autoencoders should've been a part of this video?
Sehr gut
Self-organizing maps?
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.
Good vid
Ironic that we have a Decision Tree to decide which model to use...
Radial Basis Function NN?
You missed Association Analysis.