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Machine Learning with R - Fourth Edition: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data. Brett Lantz, Brett Lantz

Machine-Learning-with-R.pdf
ISBN: 9781801071321 | 762 pages | 20 Mb

• Machine Learning with R - Fourth Edition: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data
• Brett Lantz, Brett Lantz
• Page: 762
• Format: pdf, ePub, fb2, mobi
• ISBN: 9781801071321
• Publisher: Packt Publishing

Download Machine Learning with R - Fourth Edition: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data

Google book free ebooks download Machine Learning with R - Fourth Edition: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data 9781801071321 PDB

Overview

Go on the complete R journey from tidying your data, whether small, complex, or big, to implementing and evaluating a variety of machine learning models The 10th Anniversary Edition of the bestselling R machine learning book, updated with 50% new content for R 4.0.0 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by machine learning expert Brett Lantz Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Fourth Edition provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of ML in the last few years and help you build your data science skills and tackle more challenging problems, including making successful ML models and advanced data preparation, building better learners, and making use of big data. You'll also find updates to the classic R data science book to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read. Find powerful new insights in your data; discover machine learning with R. Learn the end-to-end process of machine learning from raw data to implementation Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks Prepare, transform, and clean data using the tidyverse Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow Data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful. Introducing Machine Learning Managing and Understanding Data Lazy Learning – Classification Using Nearest Neighbors Probabilistic Learning – Classification Using Naive Bayes Divide and Conquer – Classification Using Decision Trees and Rules Forecasting Numeric Data – Regression Methods Black Box Methods – Neural Networks and Support Vector Machines Finding Patterns – Market Basket Analysis Using Association Rules Finding Groups of Data – Clustering with kmeans Evaluating Model Performance Making Successful ML Models Advanced Data Preparation Challenging Data: Too Much, Too Little, Too Complex Building Better Learners Making Use of Big Data

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