Machine Learning Foundations, Volume 1: Supervised Learning

The Essential Guide to Machine Learning in the Age of AI

Machine learning stands at the heart of today's most transformative technologies: advancing scientific discovery, reshaping industries, and transforming everyday life. From large language models to medical diagnosis and autonomous vehicles, the demand for robust, principled machine learning models has never been greater.

Machine Learning Foundations, Volume 1: Supervised Learning, offers a comprehensive and accessible roadmap to the core algorithms and concepts behind modern AI systems. Balancing mathematical rigor with hands-on implementation, this book not only teaches how machine learning works, but why it works. As part of a three-volume series, Volume 1 lays the foundation for mastering the full landscape of modern machine learning, including deep learning, large language models, and cutting-edge research.

Each chapter introduces core ideas with clear intuition, supports them with rigorous mathematical derivations where appropriate, and demonstrates how to implement the methods in Python, while also addressing practical considerations such as data preparation and hyperparameter tuning. Exercises at the end of each chapter, both theoretical and programming-based, reinforce understanding and promote active learning.

The book includes hundreds of fully annotated code examples, available on GitHub at github.com/roiyeho/ml-book, along with six comprehensive online appendices covering essential background in linear algebra, calculus, probability, statistics, optimization, and Python libraries such as NumPy, Pandas, and Matplotlib.

• Master the key concepts of supervised machine learning, including model capacity, the bias-variance tradeoff, generalization, and optimization techniques

• Implement the full supervised learning pipeline, from data preprocessing and feature engineering to model selection, training, and evaluation

• Understand key learning tasks, including classification, regression, multi-label, and multi-output problems

• Implement foundational algorithms from scratch, including linear and logistic regression, decision trees, gradient boosting, and SVMs

• Gain hands-on experience with industry-standard tools such as Scikit-Learn, XGBoost, and NLTK

• Refine and optimize your models using techniques such as hyperparameter tuning, cross-validation, and calibration

• Work with diverse data types, including tabular data, text, and images

• Address real-world challenges such as imbalanced datasets, missing data, and high-dimensional inputs

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