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I implemented 10 core ML algorithms from scratch with NumPy. Here's what no tutorial taught me [P]
A while ago I realized that intuitively understanding classical ML and calling fit() / predict() wasn't enough to feel confident in my skills or ace interviews. So I did the only thing that actually fixes that: built the algorithms from the ground up in NumPy, then validated them against Scikit-learn and PyTorch. The repo has 10 algorithms as Jupyter notebooks, each implemented as simply and directly as possible: Linear & Logistic Regression Regularization K-Nearest Neighbors Naïve Bayes Decision Tree, Random Forest Gradient Boosting, XGBoost Neural Network Three things I noticed while building these: Structure matters more than you think. A neural network becomes much clearer when you model it as a collection of blocks (linear layers and activations), each capable of a forward and backward pass. The breakdown into small pieces makes backprop feel obvious instead of complex. The same ideas keep showing up. Gradient descent isn't just one algorithm – it's the backbone of most of what's in this repo. Once you implement it by hand the first time, everything after gets easier. When something goes wrong, fixing it is the most rewarding part. You can't blame the library – you have to understand exactly what broke and why, which forces a real depth of understanding. Everything is free: https://github.com/ml-from-scratch-book/code Each notebook runs locally or opens directly in Google Colab. If you're studying for ML interviews or just want the fundamentals to feel solid, this might be useful. submitted by /u/OleksandrAkm [link] [Kommentare] reddit.com · reddit.com ↗
A while ago I realized that intuitively understanding classical ML and calling fit() / predict() wasn't enough to feel confident in my skills or ace interviews. So I did the only thing that actually fixes that: built the algorithms from the ground up in NumPy, then validated them against Scikit-learn and PyTorch. The repo has 10 algorithms as Jupyter notebooks, each implemented as simply and directly as possible: Linear & Logistic Regression Regularization K-Nearest Neighbors Naïve Bayes Decision Tree, Random Forest Gradient Boosting, XGBoost Neural Network Three things I noticed while building these: Structure matters more than you think. A neural network becomes much clearer when you model it as a collection of blocks (linear layers and activations), each capable of a forward and backward pass. The breakdown into small pieces makes backprop feel obvious instead of complex. The same ideas keep showing up. Gradient descent isn't just one algorithm – it's the backbone of most of what's in this repo. Once you implement it by hand the first time, everything after gets easier. When something goes wrong, fixing it is the most rewarding part. You can't blame the library – you have to understand exactly what broke and why, which forces a real depth of understanding. Everything is free: https://github.com/ml-from-scratch-book/code Each notebook runs locally or opens directly in Google Colab. If you're studying for ML interviews or just want the fundamentals to feel solid, this might be useful. submitted by /u/OleksandrAkm [link] [Kommentare]
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