Mastering Machine Learning with scikit-learn - Second Edition: Apply effective learning algorithms to real-world problems using scikit-learn - Brossura
Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
Gavin 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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Mastering Machine Learning with scikit-learn, Second Edition
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Mastering Machine Learning with scikit-learn, Second Edition (Paperback or Softback)
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Packt Publishing 7/27/2017, 2017
ISBN 10: 1788299876
ISBN 13: 9781788299879
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Mastering Machine Learning with scikit-learn -
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ISBN 10: 1788299876
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Digital. Condizione: New. Use scikit-learn to apply machine learning to real-world problemsAbout 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 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 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 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. Codice articolo LU-9781788299879
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