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Course Benefits

  • Learn how to implement machine learning tools to build as well as evaluate the predictors on real data
  • Learn about clustering the data by using Machine Learning clustering algorithm like K-Means, etc.
  • How to perform Prediction on the large dataset by using Regression Techniques
  • Learn the Machine Learning and its techniques in elaborated way
  • Learn how to apply the different methods to estimate the model performance by using caret package
  • learn how to the casestudy helps in forecasting the Time series data

Why Machine Learning using R?

Both Data professionals and Programmers dwell for Machine Learning technolohy specially using R. R is very helpful for the statisticians and data scientists in analyzing complex data for a better business insight. There is a great demand of Machine Learning with R in the market; the companies hire the professionals to evaluate their data to enhance their business to another extent. The professionals get high salary package and a chance to work all around the globe. 
This course will introduce you to the working of R programming language and its applications to data science. Spanning over 82 lectures with 9 hours of high quality, focused content, the course begins with providing you a basic understanding of the statistical principles like mean, median, range, etc., and then moves to datatypes and data structures in R, vectors, arrays, matrices, and more. You’ll gain good understanding of Linear Regression in Excel and R, and will learn how to visualize data in R by making use of various charts and graphs. 

Who should join
Machine Learning Using R Training?

  • Analyst professionals who have proficiency in using Machine Learning algorithms
  • Data scientist
  • Analytics Professionals
  • Software professionals
  • Course Curriculum

Prerequisites for the Course

Participants should be well versed with the foundation in R
A sound intelligence on general statistics 

Course Contents

Introduction
  • You, This course and Us 
  • Top Down vs Bottoms Up: The Google vs McKinsey way of looking at data 
  • R and Rstudio installed 
 
The 10 second answer: Descriptive Statistics
  • Descriptive Statistics: Mean, Median, Mode 
  • Our first foray into R: Frequency Distributions 
  • Draw your first plot: A Histogram 
  • Computing Mean, Median, Mode in R 
  • What is IQR (Inter-quartile Range)? 
  • Box and Whisker Plots 
  • The Standard Deviation 
  • Computing IQR and Standard Deviation in R
 
Inferential Statistics
  • Drawing inferences from data 
  • Random Variables are ubiquitous 
  • The Normal Probability Distribution 
  • Sampling is like fishing 
  • Sample Statistics and Sampling Distributions 
 
Case studies in Inferential Statistics
  • Case Study1: Football Players (Estimating Population Mean from a Sample) 
  • Case Study 2: Election Polling (Estimating Population Proportion from a Sample) 
  • Case Study 3:A Medical Study (Hypothesis Test for the Population Mean) 
  • Case Study 4: Employee Behavior (Hypothesis Test for the Population Proportion) 
  • Case Study 5:A/B Testing (Comparing the means of two populations) 
  • Case Study 6: Customer Analysis (Comparing the proportions of 2 populations) 
 
Diving into R
  • Harnessing the power of R 
  • Assigning Variables 
  • Printing an output 
  • Numbers are of type numeric 
  • Characters and Dates 
  • Logicals
 
Vectors
  • Data Structures are the building blocks of R 
  • Creating a Vector 
  • The Mode of a Vector 
  • Vectors are Atomic 
  • Doing something with each element of a Vector 
  • Aggregating Vectors 
  • Operations between vectors of the same length 
  • Operations between vectors of different length 
  • Generating Sequences 
  • Using conditions with Vectors 
  • Find the lengths of multiple strings using Vectors 
  • Generate a complex sequence (using recycling) 
  • Vector Indexing (using numbers) 
  • Vector Indexing (using conditions) 
  • Vector Indexing (using names) 
 
Arrays
  • Creating an Array 
  • Indexing an Array 
  • Operations between 2 Arrays 
  • Operations between an Array and a Vector 
  • Outer Products 
 
Matrices
  • A Matrix is a 2-Dimensional Array 
  • Creating a Matrix 
  • Matrix Multiplication 
  • Merging Matrices 
  • Solving a set of linear equations 
Factors
  • What is a factor? 
  • Find the distinct values in a dataset (using factors) 
  • Replace the levels of a factor 
  • Aggregate factors with table() 
  • Aggregate factors with tapply()
 
Lists and Data Frames
  • Introducing Lists 
  • Introducing Data Frames 
  • Reading Data from files 
  • Indexing a Data Frame 
  • Aggregating and Sorting a Data Frame 
  • Merging Data Frames 
 
Regression quantifies relationships between variables
  • Introducing Regression 
  • What is Linear Regression? 
  • A Regression Case Study: The Capital Asset Pricing Model (CAPM) 
 
Linear Regression in Excel
  • Linear Regression in Excel: Preparing the data 
  • Linear Regression in Excel: Using LINEST() 
 
Linear Regression in R
  • Linear Regression in R: Preparing the data 
  • Linear Regression in R: lm() and summary() 
  • Multiple Linear Regression 
  • Adding Categorical Variables to a linear model 
  • Robust Regression in R: rlm() 
  • Parsing Regression Diagnostic Plots
 
Data Visualization in R
  • Data Visualization 
  • The plot() function in R 
  • Control color palettes with RColorbrewer 
  • Drawing barplots 
  • Drawing a heatmap 
  • Drawing a Scatterplot Matrix 
  • Plot a line chart with ggplot2 
 

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