Post Graduate Program in Machine Learning and Artificial Intelligence

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Key Highlights

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Data Science with R

What is R? And Why R?-Different “flavors” of R-Installing R Studio DesktopUnderstanding R Studio-Installing Packages and Libraries in R Studio-Setting Your Work Directory.

Data Variables-Data Types – Operators – Keywords – ExceptionsFunctions

Vectors and Lists – Strings and Matrices – Arrays and Factors – Data Frames – Packages.

R- CSV files Read and Write and analyze the data – R- Excel files Read and Write and analyze the data

Introduction to Visualisation – Line Plots and Bar Charts – Pie Chart and Histogram – Scatter Plots and Parallel Coordinates – Advanced Plotting – Exporting Plots and Other Plotting Packages

Linear Regression Analysis – Formulation of Regression Model – Bivariate Regression – Statistics Associated with Bivariate Regression Analysis – Conducting Bivariate Regression Analysis – Multiple Regressions – Conducting Multiple Regression – Mapping Bivariate Regression with Real Time Example.

Logistic Function – Single Predictor Model – Determine Logistic Cut off – Estimated Equation for Logistic Regression

Factor Analysis Introduction – Factor Analysis Model – Statistics associated with Factor Analysis – Conducting Factor Analysis – Construction of Factor Analysis – Factor Analysis Method – Principal Component Analysis – Rotation Method – Mapping Factor Analysis with Real Time Example

Cluster Analysis Introduction – Statistics associated with Cluster Analysis – Conducting Cluster Analysis – Classification of Clustering Procedure – Hierarchical Clustering – Non Hierarchical Clustering

Association Rule Introduction – Apriori Algorithm – Multiple Association Rules – Market Basket Analysis (MBA) – Application of Apriori Algorithm and Market Basket Analysis

Naïve Bayes Introduction – Probabilistic Basics and Probabilistic Classification – Characteristics of Naïve Bayes – Real Time Case study using Naïve Bayes – Advantage and Shortcoming of Naïve Bayes

K – Nearest Neighbour Introduction – K – Nearest Neighbour Algorithm – Pre-Processing your dataset for KNN – How to measure “Nearby” – Choosing “K” and High “K” vs. Low “K” – Real Time case study using KNN – Advantage and Disadvantage of KNN

What is a Decision Tree? – How to create Decision Tree – Choosing and Identifying attributes for Decision Tree – Entropy and Information Gain with Intuitions – Pruning Trees and its types – Forward Pruning and Backward Pruning – Sub tree Replacement and Raising – Real time case study with Decision Tree

Ensample of Decision Tree.

Linear SVM using Hyperplane – Non-Linear Hyperplane using Kernal Trick and Advantage and Disadvantage of SVM

RFM Segmentation and Analysis – Propensity Modelling and its application – Churn Modelling using Operational Analytics – Fundamentals and Modelling Framework – Industry application – Market Basket Analysis using Marketing Analytics – Fundamentals and Analysis Framework – Industry Application – Price and In store Promotion using Retail Analytics – Price Elasticity and Optimization – Promotion Effectives using Analytics

SQL Database

Introduction | RDBMS | Constraints | Normalisation | Syntax | Operators | Database querie | Table queries | Indexes | Handling | Duplicates | Indexes

Data Analytics

Data Analytics across Domains | What is Analytics? | Types of Analytics | AI vs ML vs DL vs DS

Introduction to statistics and Central Limit Theorem | Measures of Central Tendancies and Measures of Spread | Descriptive Statistics with Real Time Examples | Measuring Scales | Inferential Statistics with Real Time Examples

Hypothesis Testing and Goodness of Fit test | Introduction to Statistical Tests | Statistical Test with Real Time Example | Analysis of Variance(ANOVA) & Analysis of Covariance (ANCOVA) | Probability Theory for Data Analytics | Types of Probability Distribution

Data Science with Python

Python Intro,IDE and Python Packages | Python Programming | Python Data Types – Dictionary, List and Set

Numpy Packages – Array Handling and Manupulation | Pandas Packages – Dataframe and Loading Excel, CSV File | Matplotlib Packages – Line graph and Visualisation

Histogram, Scatter Diagram, Box Plot and Bar Graph, Area Chart, Dual Axis, Array reshaping, reverse matrix, analysis

Python – Operators and String Manupulation | Control Structures(IF,IF-ELSE,IF-ELIF-ELSE, WHILE & FOR LOOP)

Python – Data Preparation Process | Python – Functions WITH and WITHOUT arguments Python – File Processing and Data Collection Methods

Python – Time Series Analysis and Forcasting | Python – Simple Predictive Analysis

Data Science with Python | Data Science Application across Multiple Domain and Business Function | Data Science Project LifeCycle

Multiple Predictive Model using Python | Python – Simple and Multiple Predictive Model in Practical | Python Correlation Analysis | Python Classifcation Model Building

Data Science – Experimental Design Analysis | Classifcation Technique – Discriminant Analysis | Data Science – Association Rule – Apriori Algorithm | Data Science – Building Recommendation System – (Market Basket Analysis)

Data Architecture Design, Data Warehousing and it’s Schema Design |Image Processing and Image Extraction | Image Processing and Object Recognition | Summarisation of Data Science Algorithm (Data Science Process)

Machine Learning

Machine Learning Introduction

Linear Regression | Logistics Regression | ANOVA and ANCOVA | Linear Discriminant Analysis

Naïve Bayes | K-Nearest Neighbour | SVM- Support Vector Machine | -Decision Tree and Random Forest

Factor Analysis | Cluster Analysis | Association Rule | Correlation

Time Series Analysis

Deep Learning

Deep Learning Fundamentals
Working of Neural Networks
Gradient Descent and Back Propagation
Activation Function

Tensorflow Introduction

Building Artificial Neural Networks (ANN)
Deep Learning-ANN-classification

intro to CNN

Introduction to RNN & Sequence prediction using RNN

Introduction to LSTM,
Sequence prediction using LSTM
Applications in text analytics , stock prediction , time series data

Natural Language Processing (NLP)

Basics of NLP | NLP- tolinisation | Removing Stop Words | Stemming & lemmatization | Parts of speech tagging | TFIDF vectorizer | Bag of words | Senmiment Analysis

Text Classification with Linear Models | Language Modelling with Probabilistic Graphical Models and Neural Networks | Word Embeddings and Topic Models | Machine Translation and Sequence-To-Sequence Models


Introduction to Reinforcement Learning | Model-Based Reinforcement Learning (Dynamic Programming)Model-Free Reinforcement Learning (SARSA, Monte Carlo, QLearning) | Approximate and Deep Reinforcement Learning (Deep Q- Learning) | Policy Gradient Reinforcement Learning | Advanced Topics on Exploration and Planning

Data Visualization using Tableau

Tableau introduction
Different types of visualization using Tableau
Tableau Dashboard Creation
Tableau Story line creation
Time series using Tableau
Different types of Joins Using Tableau
Tableau Features – Filters and format the Column
Real time project using Tableau
Tableau Highlighter
Data Blending using Tableau
Table Calculation using Tableau
Parameters and Set using Tableau
Advanced Data Preparation using Tableau
Hierarchical clustering using Tableau
Complete Course Revision using Tableau


Capstone Projects

Capstone Projects

Data Analytics

Bank Loan Modeling solution execution

Data Analytics

Application of Machine Learning Algorithm in Attrition Project and its analysis

Machine Learning

Attribution Analysis Solution Execution

Machine Learning

Bank Loan Modelling and its analysis

Machine Learning

Application of Machine Learning Algorithm in Bank Loan Modelling

Machine Learning

Merger and Acquisitions analytics

Machine Learning

LKP Project using Python

Machine Learning

Recommendation of new model

Artificial Intelligence

Telecom Churm case study using Sklearn

Artificial Intelligence

Recommendation Engine

Artificial Intelligence

Sentiment Analyser

Artificial Intelligence

Building Chatbot

Artificial Intelligence

Twitter Sentiment Analyser

Business Analysis

Online Recruitment Process

Data Visualization Tableau

Customer Loyalty Analytics and its Application

Data Visualization Tableau

Attrition Analysis and Bank Loan Modelling

Data Science using R

Solution- HR Analytics Attrition Analysis

Data Science using R

Merger and Acquisition


On completion of the Post Graduate Program in Machine Learning and Artificial Intelligence, aspirants will receive an Industry-endorsed Certificate along with additional certificate.




B.Tech and MBA ( Finance & Operation ), Ph.D in Data Analysis.


Management LDP Program, B. Tech./B.E. (Electronics & Telecommunication)



M.Tech (power electronics), MBA-HR, PhD in power quality improvement


M.B.A(Marketing & Finance )

Program Fees

LUMPSUM Fees - Rs. 35,000 + GST 18% applicable

INSTALLMENT Fees - Rs. 40,000 + GST 18% applicable

Regsitration Amount EMI 1 EMI 2 Exam Fees + GST 18%
10,000 15,000 15,000 6,000+7,200
  • Exam Fees of 6,000/- applicable for complete course.
  • EMI has to be paid every month. If not paid, fine of 500/- Rs. is applicable.

Our Faculty

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Dinesh Babu R

UrbanPro Excellence Award winner in Data Science Professional
CBAP Certified Professional
Senior Business Analyst in the MNC, Part time, Providing Business Analysis as well as Data Analysis training to both Indian as well as overseas students.
Qualification: P.hd in Data Analysis, MBA (Finance & Operation), B.Tech

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Ganesh Bhure (Management LDP Program, B.Tech/B.E. (Electronics and Telecommunication))

11yrs into training. Working on various training assignments onPython, Machine Learning, Data Analytics, Artificial Intelligence, Docker and Kubernetes, Leadership and Product Management.

Software professional with 16+yrs of experience. Handled innovation strategic roles, Technical Consultant, Technical Manager, Project Lead, Product Development, Project Management, BusinessAnalysis, Technical Design, Python, C, C++, Docker, Kubernetes, Scale Testing, Product Benchmarking, Data Analytics, Machine Learning, Artificial Intelligence, Data Science.

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Suryanarayana Murthy (MCA, B.Sc (Electronics))

9.5 Years IT Experience in Big Data Hadoop and PERL as a Developer.
5.5 Years of comprehensive experience as Big Data Developer, Practical exposure and strong knowledge in Big Data management

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Boddu Lingaiah (M.Tech (CS), B.Tech (CS))

5+ Year teaching experience in Full Stack Web Development.

Boddu Lingaiah has over 10+ Years of experience as Master Trainer at Edunet Foundation & worked as IT Faculty at KL University and Jain University.
Studied M.Tech at SKEC Karepalli as well as worked as Assistant Professior for 1.3 Year.

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Abhishek Srivastava (M.Tech (CS), MCA)

IIT Kanpur Certified Data Science Trainer
Teaching experience in Data science, Machine Learning, Deep Learning, Artificial Intelligence
Data Science Trainer with 5+ Years of experience executing Data Driven solution to increase effeciency, accuracy and utility of internal data processing. Experinced to give training on regression models, using predective data modeling and analysing Data scienceAlgorithm to deliver insights and implement action- oriented to business problems.


  • Introduction to Linux
  • Root
  • Basic commands
  • Editors
  • OS installation
  • Basic system configuration and administration
  • Understanding files and directories
  • Schedulers
  • User administration
  • Software installation