Machine Learning

This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data, and Information. 

You can also learn how R can play an important role in solving complex analytical problems. 

This module tells you what is R and how it is used by giants like Google, Facebook, etc. 

Also, you will learn the use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages. 

Business Analytics, Data, Information 

Understanding Business Analytics and R 

Compare R with other software in analytics 

Install R 

Perform basic operations in R using the command line 

Learn the use of IDE R Studio 

Use the ‘R help’ feature in R 

This module starts with the basics of R programming like data types and functions. 

In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario. 

You will also learn how to apply the ‘join’ function in SQL. 

Variables in R 

Scalars 

Vectors 

Matrices 

List 

Data frames 

Using c, Cbind, Rbind, attach and detach functions in R 

Factors 

In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis. 

Thus using and exploring the popular functions required to clean data in R. 

Data sorting 

Find and remove duplicates record 

Cleaning data 

Recoding data 

Merging data 

Slicing of Data 

Merging Data 

Apply functions 

This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a CSV file to the data scraped from a website. 

This module teaches you various data importing techniques in R. 

Reading Data 

Writing Data 

Basic SQL queries in R 

Web Scraping 

In this module, you will learn that exploratory data analysis is an important step in the analysis. 

EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process. 

Box plot 

Histogram 

Pareto charts 

Pie graph 

Line chart 

Scatterplot 

Developing Graphs 

This module touches the base of Descriptive and Inferential Statistics and Probabilities & ‘Regression Techniques’. 

Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed. 

Basics of Statistics 

Inferential statistics 

Probability 

Hypothesis 

Standard deviation 

Outliers 

Correlation 

Linear & Logistic Regression 

Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed. 

The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types. 

We will also discuss the process involved in ‘K-means Clustering’, the various statistical measures you need to know to implement it in this module. 

Introduction to Data Mining 

Understanding Machine Learning 

Supervised and Unsupervised Machine Learning Algorithms 

K- means clustering 

This module tells you about the Analysis of Variance (ANOVA) Technique. 

The algorithm and various aspects of Anova have been discussed in this module 

Additionally, this module also deals with Sentiment Analysis and how we can fetch, extract and live data from Twitter to find out the sentiment of the tweets. 

Anova 

Sentiment Analysis 

This module covers the concepts of Decision Trees and Random Forest. 

The algorithm of Random Forests is discussed in a step-wise approach and explained with real-life examples. 

Decision Tree 

Concepts of Random Forest 

Working of Random Forest