How Machine Learning Works MEAP V05 1st Edition by Mostafa Samir Abd El Fattah ISBN 1617294888 9781617294884

Machine learning is the general term for a collection of data analysis techniques that accurately and efficiently identify patterns and relationships in data and then use those models to make predictions about new data. ML drives many features of modern applications, such as tailored product recommendations, social media feeds, forecasting consumer trends, customized therapy for people with developmental challenges, and other world-changing innovations. To understand, create, and apply new ML models you need both practical skills using ML tools and libraries and a deep understanding of the theory and math under the hood.

about the book

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!