Machine Learning with R: Step by Step Guide for Newbies

English | 2018 | ISBN: 978-1720424604 | 114 Pages | EPUB | 10 MB

If you are looking for a complete beginners guide to learn Machine Learning using R, in just a few hours, this book is for you. Machine Learning is the practice of transforming data into knowledge, and R is the most popular open-source programming language used for Machine Learning. In this book, we will learn how to use the principles of Machine Learning and the R programming language to answer day-to-day questions about your data. We’ll learn about the practice of Machine Learning and the R programming language. Then, we’ll learn how to work with data to create descriptive statistics and statistical models. Finally, we’ll learn how to make predictions with machine learning.
From AI Sciences Publisher
Our books may be the best one for beginners; it’s a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations.
Several Visual Illustrations and Examples
Instead of tough math formulas, this book contains several graphs and images which detail all important R and Machine Learning concepts and their applications.
Target Users
The book designed for a variety of target audiences. The most suitable users would include:

• Beginners who want to approach Machine Learning, but are too afraid of complex math to start
• Newbies in computer science techniques and machine learning
• Professionals in Machine Learning and social sciences
• Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
• Students and academicians, especially those focusing on Machine Learning

What’s Inside This Book?

• Introduction
• Basic Functions
• Linear Regression
• Machine Learning Algorithms
• Data with R
• Generating data
• Graphical functions
• Programming with R in Practice
• Opening the Black Box
• K-nearest Neighbors
• Neural Networks
• Trees and Forests
• Standard Linear Model
• Logistic Regression
• Support Vector Machine using R
• Frequently Asked Questions
• Help! I got an error, what did I do wrong?
• Useful References