How Machine Learning Works gives you an in-depth look at the mathematical and theoretical foundations of machine learning. Seasoned practitioner Mostafa Samir Abd El-Fattah takes you step by step through a real-world ML projects. In it, you’ll learn the components that make up a machine learning problem and explore supervised and unsupervised learning. Blending theoretical foundations with practical ML skills, you’ll learn to read existing datasets using pandas, a fast and powerful Python library for data analysis and manipulation. Then, you’ll move on to choosing and implementing ML models with scikit-learn, a popular Python framework that provides a diverse range of ML models and algorithms. Along the way, you’ll be practicing important math skills, including working with probability, random variables, mean, variance, vectors, matrices, linear algebra, and statistics. You’ll also discover similarity-based methods like K-nearest neighbor and K-means clustering; decision tree-based methods like classification and regression trees; and linear methods like regularization and logical regression. Instead of simply applying black-box methods and techniques to ML problems, you’ll grok their underlying structure and apply a robust mathematical understanding alongside your practical skills. By the end of this comprehensive guide, you’ll be able to comfortably explore and understand the latest ML research as well as identify and tackle novel ML problems! what's inside Understanding machine learning problems A review of probability and statistics Similarity-based, tree-based, and linear ML methods Working with neural networks An introduction to deep learning Probabilistic models