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# Introduction
Most of the time, you learn best by building things, as is common in front-end development. I remember when I first started coding, I spent a month reading about UI/UX, HTML, and CSS, but I still couldn’t design a simple interface. This is because such learning requires practice, projects and hands-on experience.
Machine learning is different. In this field, a deep understanding of theory is more beneficial. It’s not just about using simple rules like in other areas. If you don’t understand what’s going on under the hood, it’s easy to hit roadblocks or make mistakes in your models. That’s why I strongly recommend reading high-quality books on machine learning.
This article is part of our new series where we highlight free but totally worth reading books. If you are a serious learner and want to strengthen your foundation, this list is for you. Let’s start with the first recommendation.
# 1. Understanding Machine Learning: From Theory to Algorithms
Understanding Machine Learning: From Theory to Algorithms Introduces machine learning in a rigorous but principled way, starting with the fundamental question of how to transform experience (training data) into expertise (predictive models). It ranges from basic theoretical concepts to practical algorithmic models. It gives an extensive account of the mathematics behind learning, addresses both the statistical and computational complexity of learning tasks, and covers algorithmic methods such as stochastic gradient descent, neural networks, structured output learning, as well as emerging theory such as PAC biases and compression bounds. It’s perfect for anyone who wants to go beyond using black-box models and really understand why algorithms behave the way they do.
// Outline Overview:
- Fundamentals of learning (core learning theory, probability approximately correct (PAC) learning, Vapnik-Cherovanenkos (VC) dimension, generalization, bias, complex trade-off)
- Algorithms and Optimization (Linear Prediction, Neural Networks, Decision Trees, Boosting, Stochastic Gradient Descent, Regularization)
- Model selection and practical considerations (overfitting, underfitting, cross-validation, computational efficiency)
- Discrete and generative learning (clustering, dimensionality reduction, principal component analysis (PCA), expectation-maximization (EM) algorithms, autoconvergers)
- Advanced theory and emerging topics (kernel methods, support vector machines (SVM), PACB, compression bounds, online learning, structural prediction)
# 2. Mathematics for Machine Learning
Mathematics for Machine Learning Closes the gap between the mathematical foundations and basic techniques of machine learning. It is structured in two main parts. The first part covers important mathematical tools such as linear algebra, calculus, probability and optimization. The second part shows how these tools are used in key machine learning tasks such as regression, classification, density estimation, and dimensionality reduction. Many machine learning books treat mathematics as a side topic, but this book focuses on mathematics so that readers can actually understand and build machine learning models.
// Outline Overview:
- Mathematical foundations for machine learning (linear algebra, analytic geometry, matrix decomposition, vector calculus, probability, and constant optimization)
- Supervised Learning and Regression (Linear Regression, Bayesian Regression, Parameter Estimation, Empirical Risk Minimization)
- Dimensionality reduction and unsupervised learning (PCA, Gaussian mixture model, M-algorithm, latent variable modeling)
- Hierarchical and advanced models (SVMS, kernels, separating hyperplanes, probabilistic modeling, graphical models)
# 3. Introduction to Statistical Education
Introduction to Statistical Education . It covers the main tools you’ll need, such as regression, classification, resampling (to check how good your models are), regularization (to stop things from getting crazy), tree-based methods, SVM, clustering, and even new topics like deep learning, survival analysis, and dealing with lots of tests at once. Each chapter also includes real Python-based labs so you don’t just learn the concepts, you also learn how to translate them into code.
// Outline Overview:
- Fundamentals of Statistical Learning (introduction to statistical learning, supervised vs. unsupervised learning, regression vs. classification, model validity, and the bias-variance trade-off)
- Linear and non-linear modeling (linear regression, logistic regression, general linear models, multinomial regression, splines, and general additive models)
- Advanced predictive methods (tree based methods, ensemble methods, SVM, deep learning and neural networks)
- Unsupervised and special techniques (PCA, clustering, survival analysis, censored data, and multiple testing methods)
# 4. Pattern recognition and machine learning
Pattern recognition and machine learning It teaches how machines can learn to recognize patterns from data. It begins with the basics of probability and decision making to help understand uncertainty. Then it covers important techniques such as linear regression, classification, neural networks, SVM, and kernel methods. Later, it describes more advanced models such as graphical models, mixture models, sampling methods and sequential models. The book focuses on the Bayesian approach, which helps to handle uncertainty and compare models rather than simply finding a “best” solution. Although the math can be difficult, it is perfect for students or engineers who want a deep understanding of machine learning.
// Outline Overview:
- Fundamentals of machine learning (probability theory, Bayesian methods, decision theory, information theory, and the curse of dimensionality to build a strong conceptual foundation)
- Basic models (focuses on linear regression and classification, neural networks, kernel methods, and sparse models, Bayesian approaches, regularization and optimization techniques)
- Advanced methods (graphical models, mixed models with EM, approximate estimation, and sampling methods for complex probabilistic modeling)
- Special topics and applications (continuous latent variable models (PCA, probabilistic PCA, kernel PCA), sequential data (Hidden Markov Models (HMMS), linear dynamical systems (LDS), particle filters), model combination strategies, and a practical appendix for datasets, distributions, and matrix properties)
# 5. Introduction to Machine Learning Systems
Introduction to Machine Learning Systems Shows how to build real machine learning systems – not just the model, but the entire setup that makes them work. It starts by explaining why knowing how to train a model isn’t enough: you also need to know how to get data from engineering, system design, hardware and software, how to deploy in the real world, and how to keep things working and secure. It also presents labs and emphasizes that you’ll need to think like an engineer (hardware, resource constraints, pipelines, reliability), not just a model builder. The goal is to move your language, framework, and engineering mindset from “I have a model” to “I have a working AI system that scales, is robust, and meets real needs.”
// Outline Overview:
- Foundations and Design Principles (basic architecture of machine learning systems, including introduction, machine learning workflows, data engineering, frameworks, training infrastructure)
- Performance engineering (model optimization, hardware acceleration, inference performance, benchmarking, and system-level tradeoffs)
- Robust Deployment (Machine Learning Operations (MLOPS), On-Device Learning, Security and Privacy, Robustness, Trust, Trust)
- Frontiers of Machine Learning Systems (Sustainable AI, Good for AI, Artificial General Intelligence (AGI) Systems, Emerging Research Directions)
# wrap up
These books cover key areas of machine learning, from mathematics and statistics to real-world systems. Together they provide a clear path from understanding theory to building and using machine learning models. What topics should I cover next? Let me know in the comments.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of AI with medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT.” As a 2022 Google Generation Scholar for APAC, she champions diversity and academic excellence. He has also been recognized as a Teradata Diversity in Tech Scholar, a MITACS GlobalLink Research Scholar, and a Harvard Wicked Scholar. Kanwal is a passionate advocate for change, having founded the Fame Code to empower women in stem fields.