Machine Learning with R: Expert techniques for predictive

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Solve real-world information issues with R and machine studying

Key Features

  • Third version of the bestselling, broadly acclaimed R machine studying guide, up to date and improved for R 3.6 and past
  • Harness the facility of R to construct versatile, efficient, and clear machine studying fashions
  • Learn rapidly with a transparent, hands-on information by skilled machine studying trainer and practitioner, Brett Lantz

Book Description

Machine studying, at its core, is worried with reworking information into actionable information. R presents a robust set of machine studying strategies to rapidly and simply acquire perception out of your information.

Machine Learning with R, Third Edition offers a hands-on, readable information to making use of machine studying to real-world issues. Whether you might be an skilled R consumer or new to the language, Brett Lantz teaches you the whole lot you have to uncover key insights, make new predictions, and visualize your findings.

This new third version updates the traditional R information science guide to R 3.6 with newer and higher libraries, recommendation on moral and bias points in machine studying, and an introduction to deep studying. Find highly effective new insights in your information; uncover machine studying with R.

What you will study

  • Discover the origins of machine studying and the way precisely a pc learns by instance
  • Prepare your information for machine studying work with the R programming language
  • Classify vital outcomes utilizing nearest neighbor and Bayesian strategies
  • Predict future occasions utilizing resolution timber, guidelines, and assist vector machines
  • Forecast numeric information and estimate monetary values utilizing regression strategies
  • Model advanced processes with synthetic neural networks ― the idea of deep studying
  • Avoid bias in machine studying fashions
  • Evaluate your fashions and enhance their efficiency
  • Connect R to SQL databases and rising huge information applied sciences reminiscent of Spark, H2O, and TensorFlow

Who this guide is for

Data scientists, college students, and different practitioners who want a transparent, accessible information to machine studying with R.

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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