Learning Objectives:

In this module, you will get an introduction to R, and understand why it is so popular among Data Scientists. Starting with the installation of R and its components, you will load and learn about frequently used libraries. This module touches upon data structures in R, loops and control statements in R and teaches you how to write custom functions, nested functions and functions with arguments.You will learn all about loop functions available in R which are efficient and can be written with a single command.

Going further, you will explore string manipulations and regular expressions and see how functions can be extremely useful for text or unstructured data manipulations. This module also teaches how to import data from various sources in R and also how to write files from R and connect to various databases from R. You will get an overview of visualization in R with base and ggplot libraries, and grasp the Grammarof Graphics in a very structured easy-to-understand manner. The module ends with a hands-on session on a real-life case study.

Topics Covered:

• Intro to R Programming
• Installing and Loading Libraries
• Data Structures in R
• Control & Loop Statements in R
• Functions in R
• Loop Functions in R
• String Manipulation & Regular Expression in R
• Working with Data in R
• Data Visualization in R
• Case Study

Hands-on:

• Installation of R, Libraries. Loading Libraries. Troubleshooting
• Handle various types of data structures in R
• Write control statements in R
• Write custom functions, call & pass arguments to the functions
• Use in-built loop functions in R
• Use inbuilt R functions for strong manipulations & regular expressions
• Complex Real-Life Data Manipulation, Preparation & Exploratory Data Analysis case study

Learning Objectives:

This module will take you through real-life examples of Machine Learning and how it affects society in multiple ways. You can explore many algorithms and models like Classification, Regression, and Clustering. You will also learn about Supervised vs Unsupervised Learning, and look into how Statistical Modeling relates to Machine Learning.

Topics Covered:

• Machine Learning Modelling Flow
• Types of Machine Learning
• Performance Measures
• Bias-Variance Trade-Off
• Overfitting & Underfitting
• How to treat Data in ML

Hands-on:

• Hands-on Data Manipulation
• Hands-on evaluation with performance measures of models

Learning Objectives:

This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.

Topics Covered:

• Maxima and Minima
• Cost Function
• Learning Rate
• Optimization Techniques

Hands-on :
Learn about cost-functions using R code.

Learning Objectives:

In this module you will learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies. It covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance.You will be able to grasp the concepts of Linear and Logistic Regression with real-life case studies. Through a case study on KNN Classification, you will learn how KNN can be used for a classification problem. You will further explore Naive Bayesian Classifiers through another case study, and also understand how Support Vector Machines can be used for a classification problem. The module also covers hyper-parameter tuning like regularization and a case study on SVM.

Topics:

• Linear Regression
• Case Study
• Logistic Regression
• Case Study
• K-NN Classification
• Case Study
• Naive Bayesian classifiers
• Case Study
• SVM - Support Vector Machines
• Case Study

Hands-on:

• With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
• This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers
• Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
• We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
• Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.

Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Cement the concepts learnt with a real-life case study on K-means Clustering

Topics:

• Clustering approaches
• K Means clustering
• Hierarchical clustering
• Case Study

Hands-on:

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody.This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Learning Objectives:

This module will teach you about Decision Trees for regression & classification problems through a real-life case study. You will get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID.The module covers basic ensemble techniques like averaging, weighted averaging & max-voting. You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting.

Going further, you will learn Random Forest with a real-life case study and learn how it helps avoid overfitting compared to decision trees.You will explore a real-life case study with heterogeneous ensemble machine learning techniques.You will gain a deep understanding of the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It covers comprehensive techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. Finally, you will examine a case study on PCA/Factor Analysis.

Topics Covered:

• Decision Trees
• Case Study
• Introduction to Ensemble Learning
• Different Ensemble Learning Techniques
• Bagging
• Boosting
• Random Forests
• Case Study: Heterogeneous Ensemble Machine Learning
• PCA (Principal Component Analysis) and Its Applications
• Case Study: PCA/FA

Hands-on:

• Wine comes in various types. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
• In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your resultant performance compared to a single model.
• Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives:

This module helps you to understand hands-on implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines. The courseware covers concepts like cold-start problems. You will examine a real life case study on building a Recommendation Engine.

Topics Covered:

• Introduction to Recommendation Systems
• Types of Recommendation Techniques
• Collaborative Filtering
• Content based Filtering
• Hybrid RS
• Performance measurement
• Case Study

Hands-on:

You do not need a market research team to know what your customers are willing to buy. Netflix is an example of this, having successfully used recommender system to recommend movies to its viewers. Netflix has estimated that its recommendation engine is worth a yearly $1billion.

An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build a Recommender System for a Retail Chain to recommend the right products to its users.