Machine Learning From Scratch

Machine Learning can seem intimidating - that's why most people stop at learning fit() and predict(), treating powerful algorithms as magic "black boxes". It's time to open the boxes. This book transforms anyone with basic Python and high-school math into an ML practitioner who can:

• Build 10 core ML algorithms from scratch: From Linear Regression to XGBoost and Neural Networks.
• Apply industry standards: Transition to Scikit-learn and PyTorch.
• Optimize models: Tune hyperparameters with Optuna and comprehensively evaluate models.
• Construct a practical workflow: Solve real-world ML problems.

This book begins by building a mental framework to think about ML problems. Before diving into algorithms, it introduces you to the required technical knowledge through practical examples. Next, it goes in-depth on each algorithm, following the same 5-stage framework:

1. Intuition: Understand the plain-English human thinking behind the algorithm.
2. Formalization: Translate that intuition into accessible mathematical form.
3. Implementation: Put the math into clean, from-scratch code using NumPy.
4. Test: Validate your code on real data and compare it against industry-standard libraries.
5. Tips: Learn practical strengths, weaknesses, and real-world usage insights.