Machine learning with Python cookbook : practical solutions from preprocessing to deep learning

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: ● Vectors, matrices, and arrays ● Handling numerical and categorical data, text, images, and dates and times ● Dimensionality reduction using feature extraction or feature selection ● Model evaluation and selection ● Linear and logical regression, trees and forests, and k-nearest neighbors ● Support vector machines (SVM), naïve Bayes, clustering, and neural networks ● Saving and loading trained models Who This Book Is For This book is not an introduction to machine learning. If you are not comfortable with the basic concepts of machine learning or have never spent time learning machine learning, do not buy this book. Instead, this book is for the machine learning practitioner who, while comfortable with the theory and concepts of machine learning, would benefit from a quick reference containing code to solve challenges he runs into working on machine learning on an everyday basis. This book assumes the reader is comfortable with the Python programming language and package management. Who This Book Is Not For As stated previously, this book is not an introduction to machine learning. This book should not be your first. If you are unfamiliar with concepts like cross-validation, random forest, and gradient descent, you will likely not benefit from this book as much as one of the many high-quality texts specifically designed to introduce you to the topic. I recommend reading one of those books and then coming back to this book to learn working, practical solutions for machine learning. With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book. -- Provided by Publisher Vectors, Matrices, And Arrays -- Loading Data -- Data Wrangling -- Handling Numerical Data -- Handling Categorical Data -- Handling Text -- Handling Dates And Times -- Handling Images -- Dimensionalit Reduction Using Feature Extraction -- Dimensionality Reduction Using Feature Selection -- Model Evaluation -- Model Selection -- Linear Regression -- Trees And Forests -- K-nearest Neighbors -- Logistic Regression -- Support Vector Machines -- Naive Bayes -- Clustering -- Neural Networks -- Saving And Loading Trained Models. Chris Albon. Includes Index.