The Fundamentals of Machine Learning

Aurélien Géron

AI

Part 1: The Fundamentals of Machine Learning introduces machine learning fundamentals, covering core concepts, workflows, and essential techniques for building and evaluating effective ML models.

#Machine Learning#AI#Hands-on

About The Book

Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron is a practical guide to building intelligent systems using modern machine learning tools. The book bridges theory and implementation, focusing on how to design, train, and evaluate models using Python’s most widely adopted libraries.

Rather than staying purely conceptual, Géron emphasizes hands-on workflows — from data preparation and classical machine learning algorithms to deep neural networks and production considerations. The material is especially valuable for developers and engineers who want to move from understanding machine learning concepts to applying them effectively in real projects.

This summary captures the core ideas, workflows, and key takeaways from the book.

Chapters

In Progress

1The Machine Learning Landscape

2End-to-End Machine Learning Project

3Classification

4Training Models

5Support Vector Machines

6Decision Trees

7Ensemble Learning and Random Forests

8Dimensionality Reduction

9Unsupervised Learning Techniques

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