Mastering Machine Learning With scikitlearn

Mastering Machine Learning With scikit-learn by Gavin Hackeling
2014 | ISBN: 1783988363 | English | 238 pages | EPUB | 6 MB
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

Title: Mastering Machine Learning With scikitlearn
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