Getting Started with Machine Learning with R
Getting Started with Machine Learning with R
A comprehensive guide to working on statistical data with the R language
About This Fundamentals of R Programming and Statistical Analysis Video course
- Perform basic and advanced scientific computing.
- Look into the various phases of statistical analysis using R
- Get a clear understanding of how to manipulate data by working on real-world, practical examples
Course In Detail
The R language is widely used among statisticians and data miners to develop statistical software and data analysis.
In this video course, we’ll start by diving into the different types of R data structures and you’ll learn how the R programming language handles data. Then we’ll look in-depth at manipulating different datasets in R. After that, we’ll dive into data visualization with R, using basic plots, heat maps, and networks. We’ll explore the different flow control loops of the R programming language, and you’ll learn how to debug your code.
In the second half of the course, you’ll get hands-on working with the various statistical methods in R programming. You’ll find out how to work with different probability distributions, various types of hypothesis testing, and statistical analysis with the R programming language.
By the end of this video course, you will be well-versed in the basics of R programming and the various concepts of statistical data analysis with R.
What will you learn in this course?
Course plan
Chapter 1
R Data Structures
R Data Structures
Chapter 2
Manipulating Datasets with R
Manipulating Datasets with R
Chapter 3
Visualizing Data in R
Visualizing Data in R
Chapter 4
Flow Control and Debugging Tools
Flow Control and Debugging Tools
Chapter 5
Evaluating Probability Distributions
Evaluating Probability Distributions
Chapter 6
Hypothesis Testing and Statistical Models
Hypothesis Testing and Statistical Models
Detailed course plan
Chapter 1 : R Data Structures
1h24
Lesson 1The Course Overview
Lesson 2Working with Vectors
Lesson 3Working with Lists and Attributes
Lesson 4Working with Multidimensional Arrays and Matrices
Lesson 5Working with Data Frames and Factors
Lesson 6Loading and Saving Data in R
Chapter 2 : Manipulating Datasets with R
58m20
Lesson 1Working with the Subset() and with() Functions
Lesson 2Working with the which() and grep() Functions
Lesson 3Working with the sort() and order() Functions
Lesson 4Working with sapply() and lapply()
Lesson 5Working with tapply() and table() Functions
Chapter 3 : Visualizing Data in R
1h18
Lesson 1Basic Plots in R
Lesson 2Basic Plots with the ggplot2 Package
Lesson 3Visualizing Heatmaps
Lesson 4Visualizing Networks
Lesson 5Other Visualization Methods
Chapter 4 : Flow Control and Debugging Tools
1h25
Lesson 1For Loops Versus the apply() Function
Lesson 2The if Statement and ifelse() Function
Lesson 3While and repeat Loops and the Break Statement
Lesson 4Writing Your Own Functions
Lesson 5General Programming and Debugging Tools
Chapter 5 : Evaluating Probability Distributions
43m57
Lesson 1Descriptive Statistics
Lesson 2Overview of Probability Distributions
Lesson 3Fitting Probability Distribution
Lesson 4Other Statistical Tests to Fit Distributions
Chapter 6 : Hypothesis Testing and Statistical Models
55m52
Lesson 1Model Formulas
Lesson 2One and Two Sample Tests
Lesson 3Linear Regression
Lesson 4Analysis of Variance
Lesson 5Linear Models for Gene Expression Data
