Mastering Machine Learning With scikit-learn
|Published:||Oct 29 2014|
|Posted:||Nov 19 2014|
|Book size:||3.59 MB|
Apply effective learning algorithms to real-world problems using scikit-learn About This BookDesign and troubleshoot machine learning systems for common tasks including regression, classification, and clusteringAcquaint yourself with popular machine learning algorithms, including decision trees, logistic regression, and support vector machinesA practical example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learnWho This Book Is ForIf 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. 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
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Machine Learning in Python
Incorporating machine learning in your applications is becoming essential. As a programmer this book is the ideal introduction to scikit-learn for your Python environment, taking your skills to a whole new level. Overview Use Python and scikit-learn to create intelligent applications Apply regression techniques to predict future behaviour and learn to cluster items in groups by their similarities Make use of classification techniques to perform image recognition and document classification In Detail Machine learning, the art of creating applications that learn from experience and data, has been around for many years. However, in the era of big data, huge amounts of information is being generated. This makes machine learning an unavoidable source o...
Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation About This BookLearn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really worksUse Scikit-Learn to simplify the programming side data so you can focus on thinkingDiscover how to apply algorithms in a variety of situationsWho This Book Is ForIf you're a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you. In Detail Python is quickly becoming the go-to langu...
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