Video description
Learn machine learning and data analysis from scratch with R
About This Video
 Learn data extraction and web scraping
 Learn to build custom data solutions
 Learn automating dynamic report generation
In Detail
The course covers practical issues in statistical computing that include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R programming to mastery.
We understand that theory is important to build a solid foundation, we also understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical handson examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!
R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.
By the end of the course, you’ll be a professional data scientist with R and confidently apply for jobs and will feel good knowing that you have the skills and knowledge to back it up.
Audience
This course is designed for beginners who want to learn about data science and machine learning. No prior knowledge of R is required.
Publisher resources
Table of contents
 Chapter 1 : Data Science and Machine Leaning Course Introduction
 Chapter 2 : Getting Started with R

Chapter 3 : Data Types and Structures in R
 Data Types and Structures in R Section Overview
 Basic Types
 Vectors  Part One
 Vectors  Part Two
 Vectors: Missing Values
 Vectors: Coercion
 Vectors: Naming
 Vectors: Miscellaneous
 Working with Matrices
 Working with Lists
 Introduction to Data Frames
 Creating Data Frames
 Data Frames: Helper Functions
 Data Frames: Tibbles
 Chapter 4 : Intermediate R
 Chapter 5 : Data Manipulation in R
 Chapter 6 : Data Visualization in R
 Chapter 7 : Creating Reports with R Markdown
 Chapter 8 : Building Webapps with R Shiny
 Chapter 9 : Introduction to Machine Learning
 Chapter 10 : Data Preprocessing
 Chapter 11 : Linear Regression: A Simple Model
 Chapter 12 : Exploratory Data Analysis
 Chapter 13 : Linear Regression  a Real Model
 Chapter 14 : Logistic Regression
 Chapter 15 : Starting a Career in Data Science
Product information
 Title: Data Science and Machine Learning with R from AZ Course [Updated for 2021]
 Author(s):
 Release date: April 2021
 Publisher(s): Packt Publishing
 ISBN: 9781801075282
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