Machine Learning: A Practitioner's Guide

<p>Machine Learning changes businesses and lifestyles. A Practitioner's Guide for Machine Learning combines theory and practice for beginners and experts.<br/>Machine Learning foundations relevance and applications are covered. Chapters end with applying principles to life.<br/>Python Fundamentals the second chapter teaches model-building programming.<br/>Data preprocessing includes cleansing transformation and feature engineering. Next we explore linear regression decision trees clustering and dimensionality reduction in supervised and unsupervised learning.<br/>Machine Learning teaches model performance measurement and improvement whereas Introduction to Neural Networks and Deep Learning covers sophisticated topologies.<br/>Language Processing and Reinforcement Learning applications are covered in this book. Through case studies and ethics real-world applications show impact.<br/>Future Directions predicts advanced themes and trends to enhance learning in a changing industry.<br/>About 50 Python programs in this book demonstrate Machine Learning in business finance agriculture health neurology NLP and games. These examples explain Machine Learning and show its versatility and strength across fields.<br/>Sidebars enhance learning by providing context.<br/>This book integrates theory and practice to help you explore Machine Learning a growing topic.</p>