Mastering Machine Learning with scikit-learn - Second Edition by Gavin Hackeling

Key FeaturesMaster popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networksLearn how to build and evaluate performance of efficient models using scikit-learnPractical guide to master your basics and learn from real life applications of machine learningBook DescriptionMachine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learns API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your models performance.By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.What you will learnReview fundamental concepts such as bias and varianceExtract features from categorical variables, text, and imagesPredict the values of continuous variables using linear regression and K Nearest NeighborsClassify documents and images using logistic regression and support vector machinesCreate ensembles of estimators using bagging and boosting techniquesDiscover hidden structures in data using K-Means clusteringEvaluate the performance of machine learning systems in common tasksAbout the AuthorGavin Hackeling is a data scientist and author. He was worked on a variety of machine learning problems, including automatic speech recognition, document classification, object recognition, and semantic segmentation. An alumnus of the University of North Carolina and New York University, he lives in Brooklyn with his wife and cat.Table of ContentsThe Fundamentals of Machine LearningSimple linear regressionClassification and Regression with K Nearest NeighborsFeature Extraction and PreprocessingFrom Simple Regression to Multiple RegressionFrom Linear Regression to Logistic RegressionNaive BayesNonlinear Classification and Regression with Decision TreesFrom Decision Trees to Random Forests, and other Ensemble MethodsThe PerceptronFrom the Perceptron to Support Vector MachinesFrom the Perceptron to Artificial Neural NetworksClustering with K-MeansDimensionality Reduction with Principal Component Analysis