Machine Learning using R Instructor-led Training

Machine Learning provides machines with the ability to learn autonomously based on experiences, observations and analyzing patterns within a given data set without explicitly programming. When we write a program or a code for some specific purpose, we are actually writing a definite set of instructions which the machine will follow.

Whereas in machine learning, we input a data set through which the machine will learn by identifying and analyzing the patterns in the data set and learn to take decisions autonomously based on its observations and learnings from the dataset. The first step in machine learning basics is that we feed knowledge/data to the machine; this data is divided into two parts namely, training data and testing data.

In this Machine Learning Basics Course you will -

• Get introduced to the world of machine learning with some basic concepts
• Statistics, Artificial Intelligence, Deep Learning and Data mining are few of the other technical words used with machine learning
• Learn about the different types of machine learning algorithms

Uplatz Certificate of Completion

Digital certificate - Included

Machine Learning Using R Training Syllabus

Module 1- Introduction to Data Analytics

Objectives:

• 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.

Topics

• Business Analytics, Data, Information
• Understanding Business Analytics and R
• Compare R with other software in analytics
• Install R
• Perform basic operations in R using a command line
• Learn the use of IDE R Studio
• Use the ‘R help’ feature in R

Module 2- Introduction to R programming

Objectives:

• 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.

Topics

• Variables in R
• Scalars
• Vectors
• Matrices
• List
• Data frames
• Using c, Cbind, Rbind, attach and detach functions in R
• Factors

Module 3- Data Manipulation in R

Objectives:

• 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.

Topics

• Data sorting
• Find and remove duplicates record
• Cleaning data
• Recoding data
• Merging data
• Slicing of Data
• Merging Data
• Apply functions

Module 4- Data Import Techniques in R

Objectives:

• 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.

Topics

• Reading Data
• Writing Data
• Basic SQL queries in R
• Web Scraping

Module 5- Exploratory Data Analysis

Objectives:

• 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.

Topics

• Box plot
• Histogram
• Pareto charts
• Pie graph
• Line chart
• Scatterplot
• Developing Graphs

Module 6- Basics of Statistics & Linear & Logistic Regression

Objectives:

• 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.

Topics

• Basics of Statistics
• Inferential statistics
• Probability
• Hypothesis
• Standard deviation
• Outliers
• Correlation
• Linear & Logistic Regression

Module 7- Data Mining: Clustering techniques, Regression & Classification

Objectives:

• 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.

Topics

• Introduction to Data Mining
• Understanding Machine Learning
• Supervised and Unsupervised Machine Learning Algorithms
• K- means clustering

Module 8- Anova & Sentiment Analysis

Objectives:

• 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 mine live data from Twitter to find out the sentiment of the tweets.

Topics

• Anova
• Sentiment Analysis

Module 9- Data Mining: Decision Trees and Random Forest

Objectives:

• 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.

Topics

• Decision Tree
• Concepts of Random Forest
• Working of Random Forest
• Features of Random Forest

Everyone

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