Introduction to Machine Learning with Python

Introduction

The book «Introduction to Machine Learning with Python» is a classic guide for those who want to learn the basics of machine learning using the Python language and the scikit-learn library. The authors — Andreas Mueller (one of the key developers of scikit-learn) and Sarah Guido — have created a practical guide that combines a clear presentation of theory with a large number of code examples. The book was published in 2016, but it still remains relevant for beginners in the field of Data Science.

What this book is about

The book covers the entire basic data workflow: from loading and preprocessing to building, evaluating, and tuning models. The main focus is on the scikit-learn library, but matplotlib, pandas, and numpy are also covered. The authors sequentially explain key algorithms: linear models, decision trees, random forests, support vector machines, neural networks (basic), as well as clustering methods (k-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).

Who this book is for

The book is aimed at beginner programmers familiar with the basics of Python but with no experience in machine learning. It is ideal for students, analysts, and developers who want to quickly move from theory to practice. If you are already comfortable with pandas and numpy but don't know how to apply ML algorithms, this book will be an excellent starting point.

What the reader will learn

• Basic ML concepts: supervised and unsupervised learning, overfitting, underfitting, cross-validation.
• Data preprocessing: scaling, encoding categorical features, handling missing values.
• Building and evaluating models: quality metrics (accuracy, precision, recall, F1, ROC-AUC), confusion matrix.
• Hyperparameter tuning: GridSearchCV, RandomizedSearchCV, validation curves.
• Feature selection: filters, wrappers, embedded methods (L1 regularization, feature_importances_).
• Practical examples: text classification, image analysis (handwritten digits), time series forecasting.
• Working with imbalanced data: class weighting, oversampling/undersampling.

Strengths

The main advantage of the book is its practical orientation. Each chapter contains ready-to-run code that can be immediately executed in Jupyter Notebook. The authors do not overload the reader with mathematical derivations but provide an intuitive understanding of the algorithms. The sections on model evaluation and metric selection are especially valuable — these are often overlooked in other introductory courses. The book is written in a lively style, the examples are well-structured, and the code is accompanied by detailed comments.

Who it is recommended for

The book will be useful for anyone taking their first steps in machine learning. It is suitable for self-study as well as supplementary material for university courses. If you are already familiar with ML from other books (e.g., «Python for Data Analysis» or «Hands-On Machine Learning»), this book may seem too basic, but for beginners it is one of the best options.

Overall, «Introduction to Machine Learning with Python» is a benchmark introduction to the topic that provides a solid foundation for further in-depth study. We recommend it to anyone who wants to understand how machine learning algorithms work in practice and start applying them in their projects.