Post Graduate Program in Machine Learning and Artifical Intelligence

Post Graduate Program in Machine Learning and Artifical Intelligence is one of the most booming sectors of the Computer Science field. Being one of the fastest streams of technology, the fundamental of data science is that it involves the use of automated methods to analyze gigantic amounts of data to extract valuable information from them.

India’s top ranked program  | 6 Months | Online Mode

Online Certificate Programs in India, Online Professional Courses | IIBM India

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Online Mode | 06 Months

Course Description

IIBM Institute certification course introduces you to the ever-expanding world of Data and the complex and intricate techniques that are used to store, retrieve, assess & extract tangible and profitable insights from to stay ahead of the curve of uncertainty. Learn Data Science and become rich in the all-important currency of modern times, Data.

Data Scientist Skills

Key Features

CURRICULUM

BUSINESS ANALYTICS

MACHINE LEARNING

ARTIFICIAL INTELLIGENCE

TABLEAU

DATA SCIENCE USING R

BIG DATA

Programming Language and Development Tools

Online Certificate Programs in India, Online Professional Courses | IIBM India

Meet Our Mentor

Alumni in the Industry

Program Fees

PG Program in Data Science 

Rs. 35,000 + GST

Post Graduate Program Certificate from
  • Designed for Working Professionals
  • 6-Month Online Program
  • 300+ Hours Of Online Learning Content
  • 20+ In-class & Capstone projects
  • Live Classes- 5 days in a week & one day doubt clearing session
  • Career Assistance Videos
  • Placement Assistance (Job Opportunities Portal, Hiring Drives, Resume Building & more)
  • EMI Option Available 
  • Post Graduate Program Certification from IIBM Institute along with Internship Certificate

 

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BUSINESS ANALYTICS

Fundamentals of Data Analytics

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

Basics concepts in Statistics for Data Analytics

Introduction to statistics and Central Limit Theorem, Measures of Sprea,
Descriptive Statistics with Real Time Examples, Measuring Scales,
Inferential Statistics with Real Time Examples

 Advanced concepts in Statistics for Data Analytics 


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

Python essential for Data Science


Python Intro, IDE and Python Packages, Python Programming: String, Tuple and Variable Declaration, Python Data Types – Dictionary, List and Set, Numpy Packages – Array Handling and Manuplation, 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 – Control Structures ( IF, IF-ELSE, IF-ELIF-ELSE, WHILE and FOR LOOP), Python – Data Preparation Process, Python – Functions WITH and WITHOUT arguments, Python – File Processing and Data Collection Methods, Time Series analysis and Forcasting, Simple Predictive Analysis

 Data Science with Python  


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, Assignment – Real Estate Predictive Analysis, Python Correlation Analysis, Data Science – Questionnare Design Process, Python Classication Model Building, DataScience – Experimental Design Analysis, Classication Technique – Discriminant Analysis, Data Science – Association Rule – Apripori Algorithm, Data Science – Building Recommendation System – MarketBased Analysis (MBA), Data Science – Attrition Analysis SolutionExecution, Data Science – Bank Loan Modelling Solution Execution -Part-1, Data Science – Bank Loan Modelling Solution Execution – Part-2, 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

Machine Learning Introduction and it’s Modules, Overview of Supervised Learning Algorithm, Overview of UnSupervised Learning Algorithm, How Machine Learning helps to automate the Business Process, Real Time Application of Machine Learning

 Linear Regression


Simple Linear Regression, Multiple Linear Regression, Assumptions of Linear Regression, Linear Regression Case Study, Linear Regression Project – Real Estate Model Building

Logistic Regression

 

Logistic Regression Concepts, Odds Ratio, Logit Function/ Sigmoid Function, Cost function for logistic regression, Application of logistic regression to multi-class classication, Assumption in Logistics Regression, Evaluation Matrix : Confusion Matrix, Odd’s Ratio And ROC Curve, Advantages And Disadvantages of Logistic Regression, Project Attrition and Bank Loan Modelling

 

 ANOVA and ANCOVA

 

ANOVA and ANCOVA Concepts, Coding of ANOVA, Application of ANOVA and ANCOVA

Linear Discriminant Analysis

 

Discriminant Analysis, Statistics Associated with Discriminant Analysis, Eigen Value, Case Study with Discriminant Analysis  

Naïve Bayes


Naïve Bayes Concepts, Python Execution of Naïve Bayes, Conditional Probability, Bayes Theorem, Building model using Naive Bayes, Naive Bayes Assumption, Laplace Correction, NLP with Naive Bayes

 KNN

 

Distance as Classifer, Euclidean Distance, Manhattan Distance, KNN Basics, KNN for Regression & Classifcation 

 SVM
Basics of SVM, Margin Maximization, Kernel Trick, RBF / Poly / Linear 

Decision Tree and Random Forest

 

Decision Tree Concepts, Random Forest Concepts, Decision Tree and Random Forest Coding, Decision Tree and Random Forest – Attrition Project, Decision Tree and Random Forest – Bank Loan Modelling

 Factor Analysis
Eigenvalues and Eigenvectors, Orthogonal Transformation, Using PCA 

 

Cluster Analysis

 

Clustering Methods, Agglomerative Clustering, Divisive Clustering, Dendogram, Basics of KMeans, Finding value of optimal K, Elbow Method, Silhouette Method

Association Rule

 

Apriori Algorithm, MBA – Market Basket Analysis, Multi level Association Rule, Application of Association Rule CA 

 

Correlation

 

Introduction about Correlation Analysis, Construction of Correlation Matrix, Person Product Movement Correlation, Partial Correlation, Non Metric Correlation 

 Time Series Analysis


Time Series Analysis, Data Preparation, Stationary Data, Trends /Seasonility, ARIMA Model, SARIMA & Other Models 

 Attrition Project
 Bank loan Modelling

 Merger and Acquisition Project

 LKP Project

 Complete Module Revision
Course Revision 

ARTIFICIAL INTELLIGENCE

AI with Python – Primer Concepts, Basic Concept of Articial Intelligence (AI), The Necessity of Learning AI, what is Intelligence? What is Intelligence Composed Of? What’s Involved in AI, Application of AI, Cognitive Modeling: Simulating Human Thinking Procedure, Agent & Environment

Getting Started–Why Python for AI, Features of Python, Installing Python, setting up PATH, Running Python, Script from the Commandline, Integrated Development Environment. 

Machine Learning–Types of Machine Learning (ML), Most Common Machine Learning Algorithms  

Data Preparation-Preprocessing the Data, Techniques for Data Preprocessing, Labeling the Data

Supervised Learning: Classifcation, Steps for Building a Classifer in Python, Building Classifer in Python, Logistic Regression, Decision Tree Classifer, Random Forest Classifer, Performance of a classifer, Class Imbalance Problem, Ensemble Techniques 

Supervised Learning: Regression-Building Regressors in Python 

Logic Programming, How to Solve Problems with Logic Programming, Installing Useful Packages, Examples of Logic Programming, Checking for Prime Numbers, Solving Puzzles 

Unsupervised Learning: Clustering, what is Clustering? Algorithms for Clustering the Data, Measuring the Clustering Performance, Calculating Silhouette Score, Finding Nearest Neighbors, K-Nearest Neighbors Classifer

Natural Language Processing, Components of NLP, Diculties in NLU, NLP Terminology, Steps in NLP 

NLTK package, Importing NLTK, Downloading NLTK’s Data, Installing Other Necessary Packages, Concept of Tokenization, Stemming, and Lemmatization, Chunking: Dividing Data into Chunks, Types of chunking, Bag of Word (BoW) Model, Concept of the Statistics, Building a Bag of Words Model in NLTK, Solving Problems, Topic Modeling: Identifying Patterns in Text Data, Algorithms for Topic Modeling 

Analyzing Time Series Data, Introduction, Installing Useful Packages, Pandas: Handling, Slicing and Extracting Statistic from Time Series Data, Extracting Statistic from Time Series Data, Analyzing Sequential Data by Hidden Markov Model (HMM),Example: Analysis of Stock Market data

Speech Recognition, Building a Speech Recognizer, Visualizing Audio Signals – Reading from a File and Working on it, Characterizing the Audio Signal: Transforming to Frequency Domain, Generating Monotone Audio Signal, Feature Extraction from Speech, Recognition of Spoken Words

Heuristic Search, Concept of Heuristic Search in AI, Dierence between Uninformed and Informed Search, Real World Problem Solved by Constraint Satisfaction

Gaming, Search Algorithms, Combinational Search, Minimax Algorithm, Alpha-Beta Pruning, Negamax Algorithm, Building Bots to Play Games, A Bot to Play Last Coin Standing, A Bot to Play Tic Tac Toe

Neural Networks, What is Artificial Neural Networks (ANN),Installing Useful Packages, Building Neural Networks, Perceptron based Classifier, Single – Layer Neural Networks, Multi-Layer Neural Networks

Reinforcement Learning, Basics of Reinforcement Learning, Building Blocks: Environment and Agent, Constructing an Environment with Python, Constructing a learning agent with Python 

Genetic Algorithms, What are Genetic Algorithms? How to Use GA for Optimization Problems? Installing Necessary Packages, Implementing Solutions using Genetic Algorithms 

Computer Vision, Computer Vision, Computer Vision Vs Image Processing, Installing Useful Packages, Reading, Writing and Displaying an Image, Color Space Conversion, Edge Detection, Face Detection, Eye Detection 

Tableau

Tableau – Different Types of Data Visualization – Bar chart-Connecting Tableau to a Data
File – CSV File-Navigating Tableau-Creating Calculated Fields-Adding Colors-Adding
Labels and Formatting-Exporting Your Worksheet-Time series, Aggregation, and Filters

Working with Time Series-Understanding Aggregation, Granularity, and Level of DetailCreating an Area Chart & Learning About Highlighting-Maps, Scatterplots, and Your First
Dashboard

Different Types of Joins in Tableau Dashboard and Storyline Creation – Joining Data in
Tableau-Creating a Map, Working with Hierarchies-Joining and Blending Data, PLUS: Dual
Axis Charts-Creating Bins – Tree Map Chart-Customer Segmentation Dashboard-Advanced
Dashboard Interactivity-Creating a Storyline.

Tableau Basics: Your First Bar chart – The Business Challenge – Who Gets the Annual
Bonus – Connecting Tableau to a Data File – CSV File – Navigating Tableau – Creating
Calculated Fields – Adding Colors – Adding Labels and Formatting – Exporting Your
Worksheet

Time series, Aggregation, and Filters – Working with Data Extracts in Tableau – Working
with Time Series – Understanding Aggregation, Granularity, and Level of Detail –

Creating an Area Chart & Learning about Highlighting – Adding a Filter and Quick Filter –
Joining and Blending Data, PLUS: Dual Axis Charts – Understanding how LEFT, RIGHT,
INNER, and OUTER Joins Work – Joins with Duplicate Values – Joining on Multiple Fields

Tableau – Maps, Scatterplots, and Your First Dashboard – Joining Data in Tableau –
Creating a Map, Working with Hierarchies – Creating a Scatter Plot, Applying Filters to
Multiple Worksheets

8 Real Time Project using Tableau

Let’s Create our First Dashboard! – Adding an Interactive Action – Filter – Adding an
Interactive Action – Highlighting

The Showdown: Joining Data vs. Blending Data in Tableau – Data Blending in Tableau and
Dual Axis Chart – Creating Calculated Fields in a Blend (Advanced Topic) – Section Recap

Table Calculations, Advanced Dashboards, Storytelling – Downloading the Dataset and
Connecting to Tableau – Mapping: how to Set Geographical Roles – Creating Table
Calculations for Gender – Creating Bins and Distributions for Age –

Leveraging the Power of Parameters – How to Create a Tree Map Chart – Creating a
Customer Segmentation Dashboard – Advanced Dashboard Interactivity – Analyzing the
Customer Segmentation Dashboard – Creating a Storyline

Advanced Data Preparation – What Format Your Data Should Be In – Data Interpreter and
Pivot – Splitting a Column into Multiple Columns – Metadata Grid – Fixing Geographical Data
Errors in Tableau

Hierarchical Clustering Using Tableau Software and K-Mean Clustering Using Tableau
Software

Complete Course Revision using Tableau Software and Steps to execute the Real Time
Project using Tableau

DATA SCIENCE USING R

R – Introduction: 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.

R Implementation – Data Variables-Data Types – Operators – Keywords – ExceptionsFunctions
R Data Structures – Vectors and Lists – Strings and Matrices – Arrays and Factors – Data
Frames – Packages.
R Interfaces – R- CSV files Read and Write and analyze the data – R- Excel files Read and
Write and analyze the data

Data Visualization Using R Software- 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

Predictive Customer Analytics using R – Linear Regression using R Software – 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.

Bank Loan Modelling using R – Logistic Function – Single Predictor Model – Determine
Logistic Cut off – Estimated Equation for Logistic Regression

Sales Promotion Effectiveness -Dimension Reduction using R Software – Factor Analysis –
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

Customer and Market Segmentation – Cluster Analysis using R Software – Cluster Analysis
Introduction – Statistics associated with Cluster Analysis – Conducting Cluster Analysis –
Classification of Clustering Procedure – Hierarchical Clustering – Non Hierarchical Clustering .

Retail Analytics: Market Basket Analysis (MBA) – Association Rule using R Software –
Association Rule Introduction – Apriori Algorithm – Multiple Association Rules – Market
Basket Analysis (MBA) – Application of Apriori Algorithm and Market Basket Analysis

Customer Loyalty Analytics- Naïve Bayes Classification using R Software – 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 (KNN) Using R Software – 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

Decision Trees using R Software – 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

Random Forest using R Software – Ensample of Decision Tree.

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

Real Time Project – Customer Loyalty Analytics and its Application using R Software- 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

Real Time Project – Finance Analytics and its Application using R Software- Credit Risk
Analytics using Logistic Regression – Merger and Acquisitions Analysis using Financial
Analytics – Company’s Short Term Performance Analysis using Analytics – Company’s Long
Term Performance Analysis Using Analytics – Financial Ratio Analysis using Analytics –
Profitability Ratio Analysis using Financial Analytics – Leverage Ratio and Liquidity Ratio
Analysis using Financial Analytics – Efficiency and Valuation Ratio Analysis using Financial
Analytics – Company’s Financial Analytics – Sector wise Analysis Using Financial Analytics –
Industry wise Analysis Using Financial Analytics – Individual Company Analysis Using
Financial Analytics – Loss Mitigation and Revenue Growth Analysis

LINUX

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