Mastering Machine Learning with scikit-learn (Repost)

Apply effective learning algorithms to real-world problems using scikit-learn About This Book Design and troubleshoot machine learning systems for common tasks including regression, classification, and clustering Acquaint yourself with popular machine learning algorithms, including decision trees, logistic regression, and support vector machines A practical example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learn Who This Book Is For If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. What You Will Learn Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metrics Predict the values of continuous variables using linear regression Create representations of documents and images that can be used in machine learning models Categorize documents and text messages using logistic regression and support vector machines Classify images by their subjects Discover hidden structures in data using clustering and visualize complex data using decomposition Evaluate the performance of machine learning systems in common tasks Diagnose and redress problems with models due to bias and variance