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Foundations of Machine Learning: A Practitioner's Journey - From Mathematical Foundations to Classical Algorithms
Paperback
Series: The Practitioner's Journey, Book 1
Publisher: Independently Published
Published: Apr 13 2026
Pages: 498
Weight: 1.88
Height: 1.00 Width: 7.00 Depth: 10.00
Language: English
The first volume of A Practitioner's Journey. Eighteen chapters take you from linear algebra and probability through every classical machine-learning algorithm worth knowing - regression, trees, ensembles, SVMs, KNN, time series, and recommendation systems - with the math, the intuition, and runnable code, all in one place.
This is the curriculum a working ML practitioner actually needs. Most intro to ML books pick a side: pure math with no code, or library-call tutorials that fall apart the moment you try to apply them. Foundations of Machine Learning refuses both. Every chapter is built around a working scenario. Every code example runs. Every concept comes with both the math and the intuition.
You will learn to:
- Reason about linear algebra, calculus, probability, and optimization the way ML uses them
- Derive and implement classical algorithms from first principles, not as library calls
- Choose the right algorithm for the right problem and explain why
- Evaluate models honestly, avoid overfitting, and know when good enough is good enough
- Apply the CRISP-DM framework to a real end-to-end case study
Companion volumes: Book 2 Machine Learning in Production covers deep learning, computer vision, and the production engineering stack. Book 3 Artificial Intelligence in Production covers LLMs, RAG, agents, and modern AI infrastructure.
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