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Brett Lantz - Machine Learning with R, Second Edition [2015, PDF, ENG]

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Post 30-Sep-2016 01:00

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Machine Learning with R, Second Edition
Год издания: 2015
Автор: Brett Lantz
Жанр или тематика: Программирование
Издательство: Packt Publishing
ISBN: 9781784393908
Язык: Английский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 452
Описание: Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience.
With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.
What You Will Learn
- Harness the power of R to build common machine learning algorithms with real-world data science applications
- Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
- Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
- Classify your data with Bayesian and nearest neighbor methods
- Predict values by using R to build decision trees, rules, and support vector machines
- Forecast numeric values with linear regression, and model your data with neural networks
- Evaluate and improve the performance of machine learning models
- Learn specialized machine learning techniques for text mining, social network data, big data, and more

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Table of Contents
1: Introducing Machine Learning
2: Managing and Understanding Data
3: Lazy Learning – Classification Using Nearest Neighbors
4: Probabilistic Learning – Classification Using Naive Bayes
5: Divide and Conquer – Classification Using Decision Trees and Rules
6: Forecasting Numeric Data – Regression Methods
7: Black Box Methods – Neural Networks and Support Vector Machines
8: Finding Patterns – Market Basket Analysis Using Association Rules
9: Finding Groups of Data – Clustering with k-means
10: Evaluating Model Performance
11: Improving Model Performance
12: Specialized Machine Learning Topics
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