ML from Zero: Machine Learning, Math & Python from Scratch

About this course

This course contains the use of artificial intelligence.

The lecture narration in this course is produced with a neural text-to-speech voice. Every other part of it is mine: the curriculum, the mathematical derivations, the from-scratch implementations, the projects, the exercises and the test suite were all written and verified by hand.

Every other ML course teaches you to call .fit(). This one teaches you to write it.

Eleven times, for eleven algorithms, in pure NumPy.

Here is the problem with knowing only the API. When your accuracy is 99% and your manager is suspicious. When the model works in your notebook and dies in production. When someone asks why you chose this algorithm and not that one. If all you have ever done is call .fit(), you are guessing.

Every algorithm follows the same six steps:1. The math on the whiteboard, derived rather than hand-waved, and always finished with a worked numeric example.2. From-scratch code, line by line, with nothing hidden.3. The vectorized version, the way it is really done, with the speed difference measured.4. A real dataset: load, train, evaluate, visualize.5. Validated against scikit-learn, side by side, so you know your code is right.6. A coding exercise and a quiz, with tests that pass or fail.

What you will build: linear regression, gradient descent, logistic regression, k-nearest neighbours, decision trees, random forests, Naive Bayes, k-means, PCA, and a neural network with backpropagation derived by hand.

Three capstone projects: house-price regression end to end, a classifier on badly imbalanced data, and customer segmentation with k-means and PCA.And an honest benchmark. The final section puts your code next to scikit-learn on accuracy and on speed, including where we lose by 15 times and exactly why (they use a k-d tree; we do not). Courses that only show wins are marketing. This one shows the gaps and explains them.<

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