Mastering Machine Learning with Scikit-Learn -

2017 | Computing & IT

Use scikit-learn to solve real-world problems with machine learning About This Book * Get beyond the basics and design efficient machine learning systems using scikit-learn * Master the popular machine learning algorithms, including decision trees, logistic regression, and support vector machines * A practical, example-based guide to help you build and evaluate efficient models in scikit-learn and improve their performance Who This Book Is For This book is for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them. This book is for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required.

What You Will Learn * Review fundamental concepts such as bias and variance * Extract features from categorical variables, text, and images * Predict the values of continuous variables using linear regression and K Nearest Neighbors * Classify documents and images using logistic regression and support vector machines * Create ensembles of estimators using bagging and boosting techniques * Discover hidden structures in data using K-Means clustering * Evaluate the performance of machine learning systems in common tasks In Detail This book examines machine learning models including k-nearest neighbors, logistic regression, naive Bayes, random forests, and support vector machines. You will work through document classification, image recognition, and other example problems. This book reviews fundamental machine learning concepts such as the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve performance.

Through the book's examples you will become familiar with scikit-learn's API, and learn to use it to solve even the complex data problems with ease.

Gavin Hackeling (Author)