Mastering Machine Learning with scikit-learn - Second Edition: Apply effective learning algorithms to real-world problems using scikit-learn

Key Features • Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks • Learn how to build and evaluate performance of efficient models using scikit-learn • Practical guide to master your basics and learn from real life applications of machine learning Book Description Machine 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-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s 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 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 Use scikit-learn to apply machine learning to real-world problemsAbout This BookMaster 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 learningWho This Book Is ForThis book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and 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 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 tasksIn DetailMachine 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-learn's API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model's 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.Style and approachThis book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems. Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and 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 Machine 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-learn's API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model's 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. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and.. 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 In Detail This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models. The book will also walk you through an example project that prompts you to label the most uncertain training examples. You will also use an unsupervised Hidden Markov Model to predict stock prices. By the end of the book, you will be an expert in scikit-learn and will be well versed in machine learning