Scikit-Learn and TensorFlow. Machine Learning with Python

Introduction

The book "Scikit-Learn and TensorFlow. Machine Learning with Python" (original title Hands-On Machine Learning with Scikit-Learn and TensorFlow) is one of the most authoritative and practice-oriented guides to machine learning (ML) and deep learning (DL) in Python. The author, Aurélien Géron, a former Google engineer, has managed to create a textbook that is equally useful for beginners taking their first steps in Data Science and for experienced developers looking to deepen their knowledge. The third edition (2022) has been completely revised and updated for modern library versions, including TensorFlow 2.x and Scikit-Learn 1.x.

What This Book Is About

The book covers the complete data workflow: from problem formulation and data preprocessing to building, evaluating, and deploying models. All material is divided into two large parts:

• Part I. Fundamentals of Machine Learning — dedicated to classical algorithms: linear and logistic regression, SVM, decision trees, ensemble methods (Random Forest, Gradient Boosting), clustering (K-Means, DBSCAN), and dimensionality reduction methods (PCA). All examples are implemented using the Scikit-Learn library.
• Part II. Deep Learning and Neural Networks — covers the theory and practice of neural networks: from a simple perceptron to convolutional (CNN) and recurrent (RNN) networks, as well as transformers and generative adversarial networks (GANs). The second part is entirely built on TensorFlow 2.x and Keras.

Special attention is paid to practical aspects: working with real datasets, hyperparameter tuning, regularization, data augmentation, and deploying models using TensorFlow Serving and TF Lite.

Who This Book Is For

The book is intended for a wide audience:

• Beginning Data Science professionals — those already familiar with the basics of Python and wanting to systematically study machine learning.
• Programmers transitioning to ML — Python developers who want to apply their skills in the field of artificial intelligence.
• Technical students — for in-depth study of ML theory and practice.
• Experienced ML engineers — as a reference and source of best practices, especially in the area of deep learning.

What the Reader Will Learn

After reading the book, you will be able to:

• Understand and implement basic machine learning algorithms using Scikit-Learn.
• Build and train neural networks of any complexity using TensorFlow and Keras.
• Properly evaluate model quality (cross-validation, metrics, confusion matrix).
• Apply regularization methods to combat overfitting.
• Work with images, text, and time series.
• Deploy models to production using TensorFlow Serving.
• Use modern architectures (transformers, GANs) to solve complex problems.

Strengths of the Book

The book stands out from other textbooks for several reasons:

• Practical focus — each chapter contains ready-to-run code examples that can be immediately applied to your own data.
• Relevance — the third edition was released in 2022 and covers the latest library versions, including TensorFlow 2.9 and Scikit-Learn 1.1.
• Clear writing style — complex concepts are explained in simple language, with many illustrations and analogies.
• Depth — the author not only shows how to call a function but also explains why the algorithm works the way it does, with mathematical background.
• Structure — logical progression of material: from simple to complex, with clear summaries and exercises at the end of each chapter.

Who We Recommend It To