Machine Learning with R for Data Science

Best fit program for tech & non-tech graduates and working professionals to create a new career in your own industry as data analyst.

Program Duration

1 ½ months

Daily or weekend

3 – 4 hours per day

Program covers

R Programming
Machine Learning with R for Data Science

Detailed course

R Programming

Introduction to R
• Math, Variables, and Strings
• Vectors and Factors
• Vector operations

Data structures in R
• Arrays & Matrices
• Lists
• Data frames

R programming fundamentals
• Conditions and loops
• Functions in R
• Objects and Classes
• Debugging

Working with data in R
• Reading CSV and Excel Files
• Reading text files
• Writing and saving data objects to file in R

Strings and Dates in R
• String operations in R
• Regular Expressions
• Dates in R

Machine Learning for Data Science & Analytics

Machine learning vs. Statistical modelling

Supervised vs. Unsupervised Learning
• Machine Learning Languages, Types, and Examples
• Machine Learning vs Statistical Modelling
• Supervised vs Unsupervised Learning
• Supervised Learning Classification
• Unsupervised Learning

Supervised Learning
• Understanding nearest neighbour classification
• The KNN algorithm
• Measuring similarity with distance
• Choosing Appropriate K
• Use Case

Classification Using Naïve Bayes
• Basic Concepts of Bayesian Methods
• Probabilistic Learning

Classification using Decision Trees
• The C5.0 decision tree algorithm
• Understanding Classification Rules
• Separate and Conquer
• Rules from decision trees
• Advantages & Disadvantages of Decision Trees

Understanding Regression
• Simple Linear Regression
• Ordinary least Square estimation


Multiple Linear Regression

Support Vector Machines
• Classification with Hyper planes
• Using Kernels for non-linear spaces

Neural Networks
• Black Box Methods
• Training neural networks with back propagation

Unsupervised Learning

Association Rules – Pattern detection
• K-Means Clustering plus Advantages & Disadvantages
• Hierarchical Clustering plus Advantages & Disadvantages
• Measuring the Distances Between Clusters – Single Linkage Clustering
• Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
• Density-Based Clustering

Evaluating Model Performance

Improving Model Performance

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