Description

Machine Learning Algorithms in Depth.

Master Machine Learning Algorithms from Theory to Real-World Implementation

Gain a deeper understanding of how machine learning algorithms work internally so you can build better models, diagnose problems, and improve performance with confidence. This comprehensive guide takes you beyond using ML libraries by explaining the mathematics, design principles, and practical implementations behind modern machine learning techniques.

Book Overview

For serious machine learning practitioners, understanding what happens inside an algorithm is essential. Machine Learning Algorithms in Depth provides a detailed exploration of the most important ML algorithms, helping you understand their foundations, analyze their behavior, and apply them effectively to real-world problems.

Starting with the mathematical principles behind machine learning, the book introduces key concepts in probability, Bayesian inference, optimization, and algorithm design. You’ll learn how different approaches work internally and how they can be implemented using Python.

The book covers a wide range of applications across finance, computer vision, natural language processing, and data analysis. Each algorithm is explained through mathematical derivations, practical examples, implementation details, code explanations, and visual illustrations that make complex concepts easier to understand.

You’ll explore advanced topics including probabilistic models, deep learning techniques, clustering methods, anomaly detection, optimization strategies, and ensemble approaches. By connecting theory with practical implementation, this book helps you develop the skills needed to troubleshoot models, improve accuracy, and design more effective machine learning solutions.

Key Topics Covered

• Monte Carlo methods for stock price simulation
• Image enhancement through mean-field variational inference
• Expectation-Maximization (EM) algorithms for Hidden Markov Models
• Imbalanced learning, active learning, and ensemble techniques
• Bayesian optimization for improving model parameters
• Dirichlet Process K-Means for clustering applications
• Financial clustering using inverse covariance estimation
• Simulated annealing for optimization problems
• Image retrieval using ResNet convolutional neural networks
• Time-series anomaly detection with variational autoencoders

What You’ll Learn

• Understand the mathematical foundations behind major machine learning algorithms
• Implement machine learning techniques using Python
• Apply Bayesian methods, probabilistic models, and deep learning approaches
• Analyze and optimize ML models for better performance
• Work with supervised and unsupervised learning techniques
• Explore classification, regression, clustering, and neural network algorithms
• Understand core machine learning data structures and algorithmic strategies
• Solve practical problems across different industries and applications

Who This Book Is For

This book is designed for machine learning engineers, data scientists, AI practitioners, and developers who want to strengthen their understanding of ML algorithms beyond basic usage.

Readers should have familiarity with linear algebra, probability, and introductory calculus concepts to fully benefit from the mathematical explanations and implementations.

About the Author

Vadim Smolyakov is a data scientist working on enterprise and security research and development at Microsoft.

Table of Contents

Part 1: Machine Learning Foundations

1. Machine Learning Algorithms
2. Markov Chain Monte Carlo
3. Variational Inference
4. Software Implementation

Part 2: Supervised Learning

5. Classification Algorithms
6. Regression Algorithms
7. Advanced Supervised Learning Methods

Part 3: Unsupervised Learning

8. Fundamental Unsupervised Learning Algorithms
9. Advanced Unsupervised Learning Techniques

Part 4: Deep Learning

10. Core Deep Learning Algorithms
11. Advanced Deep Learning Algorithms