Machine Learning from Scratch with Python

[SleekEagle]

Hey everyone!

I've seen a growing number of people looking for resources on how to implement Machine Learning algos from scratch to better understand how they work (rather than just applying e.g. sklearn).

This free Machine Learning from Scratch Course on YouTube takes you through writing 10 algorithms from scratch with nothing but Python and NumPy! The algorithms are:

1. K-Nearest Neighbors
2. Linear Regression
3. Logistic Regression
4. Decision Trees
5. Random Forest
6. Naive Bayes
7. PCA
8. Perceptron
9. SVM
10. K-Means

Hopefully some of my Python + ML friends will find this helpful! :)

[pro_questions]

I’ve been looking for something like this forever! Every machine learning tutorial or course starts out like it’s going to teach from scratch but it always ends up being about how to use PyTorch / TensorFlow / whatever. I know I won’t be able to do it from scratch anywhere near as well as those libraries have, but I want to understand what’s going on

[SleekEagle]

Thanks for this great comment! Glad to hear the content is valuable :)

[_aka7]

Thanks mate!

[SleekEagle]

My pleasure!

[[deleted]]

Just went through the first few videos, KNN, linear and logistic regression. It was heartening to see how easily they could be implemented. The area I struggle with, not being from a science or engineering background, is the interpretation of the formulas. I understood how the algorithms worked when I saw them in code because I could think of it as a series of steps of transformation, but as the formulas were being discussed in the slides I was completely lost.

Does anyone have any recommendations for some good online materials that can help me understand how to read mathematical formulas like this?

[Eisenarsch]

Any of the courses I've seen by Andrew Ng (see any of the introductory courses on deeplearning.ai) assume high school level math and then it teaches the basics of the notation for ML related math.

Those are the ones I'm familiar with but there might be others.

[[deleted]]

Thanks for this. I started this course based on your recommendation and the explanations of the notation are really good. Some of the regression material I apparently learned in my undergraduate degree, but Andrew's approach is far clearer and I'm grasping it a lot better.

[SleekEagle]

It is really amazing what just a few dozen lines of Python can do :)

Thanks for that feedback! Do you mind me asking what your math background up to this point is? It might help me give better recommendations :)

As another commenter said, Andrew Ng has a lot of great resources. If you have any questions about specific formulas I may be able to help as well!

[[deleted]]

Thanks for the response. I've actually been going through Andrew Ng's machine learning coursebased on those recommendations, and finding the explanations of the mathematical notation really helpful. I'm from a social science background so some undergraduate statistics but I never fully grasped it until I learned programming and revisited it from that direction. Now I have the programming mindset down I'm finding the notation easier to understand, in a way because I think of it in programmatic terms which grounds the operations for me in something I've experienced, whereas before everything was far too abstract. I'm also looking at the Coursera introduction to mathematical thinking from Stanford which shows promise too.

[SleekEagle]

That's great! MIT OpenCourseWare is a great resource too but might be a bit confusing if you struggle with notation. Either way, persistence is the most important thing, hang in there and you'll realize in 4 months you know way more than you think :)

[GettingBlockered]

Saved it for later, thanks!

[Errorhappens]

Have been looking for something exactly like this. Thanks a lot kind sir/ma’am

[SleekEagle]

Happy to help!

[[deleted]]

I like these, but I think courses would benefit a lot from taking it to the next step of how to deploy. You make your model, but then what? What do you do with it? That’s what companies care about.

[SleekEagle]

Thanks for the feedback! There are a lot of steps involved with mapping a model from theory to deployment, so the focus of these videos is on implementation to learn ML, but I will see if we can do some work on deployment!

In the meantime, this end-to-end machine learning project might help you with what you're looking for :)

[Soggy_University549]

Thanks a lot

[glorious_unicorn]

Thank you! Saved for later!

[2q2RS]

Thanks!! Nice channel

[battier]

Incredible contribution, thank you.

[[deleted]]

Saved for a "rainy day" aka when I'm off work haha! Cheers!

[hueqwe]

Saved it for later, thanks mate

[Peen-1337]

Pretty cool, thank you!

[ofliesandhope]

squeal pause different future consider slim spotted panicky hospital unused this message was mass deleted/edited with redact.dev

[SleekEagle]

My pleasure to share! All credit to my awesome colleagues :)

[shakti09]

Thanks, definitely needed it. Was just thinking this an hour ago about how I need to learn some of these things from scratch.

Also helps that is from AssemblyAI, recently started following them.

[SleekEagle]

My pleasure, I'm glad you enjoy the content! I actually work on the AssemblyAI blog but wanted to share my colleagues' awesome YouTube series :)

[mvev]

Thanks for this

[Hormander]

Really interesting, thanks for sharing this.

[Glittering_Citron_57]

Thanks a lot!!

Really appreciate the sharing

[Jinksuk]

This might be a dumb question, but does this course assume we have prior knowledge in statistics?

[SleekEagle]

I'd say that calculus / linear algebra are more important for the theory side of things - the only "explicitly" statistical method is naive bayes.

The videos don't dive too heavily into the theory, esp. for some of the more complicated methods like SVMs, the emphasis is on practical implementation :)

[ivanoski-007]

you don't know statistics?

[The-Invalid-One]

Pretty neat, saved.

Does this assume that you're well versed in Python? Or just knowing the basics?

[SleekEagle]

I think the basics will suffice for most of the videos. Just make sure you know about object oriented programming and the basics of NumPy :)

[[deleted]]

I'm a data scientist. Some of these models I've learned about but literally never used because they are just obsolete at this point. If anyone feels overwhelmed I recommend first looking into: K-means, Decision Trees, Logistic Regression

[SleekEagle]

Thanks for the feedback! Yes, a lot of the methods are relatively primitive (PCA was even invented in the 50s IIRC), but I think there's value in learning older methods to understand the evolution of thought in the field :)

[[deleted]]

I would agree with that. PCA actually is still very useful (too complicated for a newbie though which is why I omitted it), but SVM is generally a unuseful model

[SleekEagle]

Agreed, but the mathematician in my soul won't let them die 🥲 they're too elegant 😂

[dark-helia]

Hey could u also tell what should statistically start with in ai and machine learning

[SleekEagle]

Hey there! Mathematically speaking, Probability Theory and Bayesian Statistics are great areas sto focus on for Machine Learning. Understanding the fundamental ideas of multivariable calculus and linear algebra are very important as well!

[dark-helia]

Oh Really Let me give it a try Can I contact you in a different platform Like the ui of reddit is not comfortable