Finally, A Roadmap for Machine Learning with R!
Machine Learning Made Easy with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating machine learning models with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R.
NO EXPERIENCE REQUIRED: This easy reading book uses plain language rather than a ton of equations; I’m assuming you never did like linear algebra, don’t want to see things derived, dislike complicated computer code, and you’re here because you want to try successful machine learning algorithms for yourself.
YOUR PERSONAL ROADMAP: Through a simple to follow intuitive step by step process, you will learn how to use the most popular machine learning algorithms using R. Once you have mastered the process, it will be easy for you to translate your knowledge to assess your own data.
THIS BOOK IS FOR YOU IF YOU WANT:
• Explanations rather than mathematical derivation.
• Practical illustrations that use real data.
• Worked examples in R you can easily follow and immediately implement.
• Ideas you can actually use and try out with your own data.
TAKE THE SHORTCUT: This book was written for people who want to get up to speed as quickly as possible.
In this book you will learn how to:
• Unleash the power of Decision Trees.
• Develop hands on skills using k-Nearest Neighbors.
• Design successful applications with Naive Bayes.
• Deploy Linear Discriminant Analysis.
• Explore Support Vector Machines.
• Master Linear and logistic regression.
• Create solutions with Random Forests.
• Solve complex problems with Boosting.
• Gain deep insights via K-Means clustering.
• Acquire tips to enhance model performance.
Overview of Book
Chapter 1: Introduction to Machine Learning
Chapter 2: Decision Trees
Chapter 3: k-Nearest Neighbors
Chapter 4: Naive Bayes Classifier
Chapter 5: Linear Discriminant Analysis
Chapter 6: Linear Regression
Chapter 7: Logistic Regression
Chapter 8: Support Vector Machines
Chapter 9: Random Forests
Chapter 10: Boosting
Chapter 11: K- Means Clustering
Chapter 12: Tips to Enhance Performance
GET STARTED TODAY! Everything you need to get started is contained within this book. Neural Networks for Time Series Forecasting with R is your very own hands on practical, tactical, easy to follow guide to mastery.
Buy this book today and accelerate your progress!