Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits (ebook)

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits (ebook) - Opis

Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production. Spis treści: 1. Introduction to Machine Learning & Scikit-Learn2. Making Decisions with Trees3. Making decisions with linear equations4. Preparing Your Data5. Image processing with nearest neighbors6. Text Classification - Not all data exists in tables7. (...) więcej Neural Networks - Here comes the Deep Learning8. Ensembles - When one model is not enough 9. The Y is as important as the X10. Imbalanced Learn - Not even 1% win the lottery 11. Clustering - Grouping data when no correct answers are provided 12. Anomaly Detection - Finding Outliers in Data13. Recommender System - Learning about users’ taste from their previous interactions O autorze: Tarek Amr has 8 years of experience in data science and machine learning. After finishing his postgraduate degree at the University of East Anglia, he worked in a number of startups and scale-up companies in Egypt and the Netherlands. This is his second data-related book. His previous book covered data visualization using D3.js. He enjoys giving talks and writing about different computer science and business concepts and explaining them to a wider audience. He can be reached on Twitter at @gr33ndata. He is happy to respond to all questions related to this book. Feel free to get in touch with him if any parts of the book need clarification or if you would like to discuss any of the concepts here in more detail. mniej