Masters in Generative AI

Master's Program 5 out of 5 rating votes 4254

Masters in Generative AI

INR 95000

Excluding GST

100% Placement


Ranked#2 among Top Full-Time

Masters in Generative AI in India- 2010-2024

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Start Date : 24th Jun 2024Duration : 9 Months
Format : Live Online /Self-Paced/Classroom
Future of Masters in Generative AI

₹The Average Salary of 1.56 Lakh

The average salary for a generative AI professional in India is around INR 156,000 throughout the year. Generative AI professionals earn a higher average salary than traditional data analytics roles.


Number of Jobs Available

Generative AI can be used for a wide range of applications, including image generation, video synthesis, text generation, music composition, and even drug discovery. It is being used in industries such as fashion, gaming, healthcare, and entertainment to create new content, prototypes, and solutions.


Future Aspects

Generative AI will change the way we use technology and society functions. Key trends include smarter language models, the ability to combine different types of AI, and customise the experience for users. It is also important to think about ethics, build AI models for specific industries, and use AI in real-time situations. Tools that enable human-AI collaboration and leverage AI across industries are also becoming more and more common.


Masters in Generative AI

5 out of 5 rating vote 4254

Masters in Generative AI.

INR 95000 + GST
100% Placement Assistance

Program Overview

Generative AI aims to teach computers to be creative. The Master's program in Generative AI is your ticket to mastering this cutting-edge field. You will learn how to get computers to think creatively, analyze data, and generate new content like images, music, and even stories. This course takes you deep into the world of artificial intelligence and machine learning. From understanding the fundamentals of AI to mastering advanced techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), you will gain comprehensive skills to solve real-world challenges.

  • Roles you can take on: Upon completing the program, you can take on exciting roles like: - AI Creative Developer, Data Artisan, AI Consultant, Digital Artist, AI Researcher, Content Creator, and AI Educator.

Key Benefits:

  • Web_IconCreativity Amplification: A Master's in Generative AI fosters creativity by helping you develop new ideas and solutions, like computer-aided brainstorming, but with a digital twist.
  • branTime and Cost Savings: Automating tasks saves you time and money, so you can focus on more important tasks instead of repetitive ones.
  • polyHyper-personalization: Experiences are tailored to you, for example suggesting movies or songs based on your preferences.
  • analyticsEnhanced Efficiency and Productivity: It helps you get things done faster and better, like a super-efficient assistant that never tires.

Key Points:

m IconData Analytics and Data Structures: Cover the basics of data analysis and data structures, laying the foundation for understanding and effectively processing data.

 m IconIntroduction of Linear Algebra: Linear algebra serves as the mathematical backbone of many machine learning algorithms. Learn fundamental concepts such as vectors, matrices, and linear transformations to gain a solid foundation in advanced mathematical modeling.

icon 3Introduction to Machine Learning: Get ready to dive into the fascinating world of machine learning. Learn about different types of machine learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning, and understand how they can be used to solve real-world problems.

From designing virtual worlds to composing music to creating art, the possibilities of generative AI are endless. As industries continue to adopt AI-driven solutions, the demand for skilled professionals will only grow. A Masters in Generative AI will provide you with the best prerequisites to build a rewarding career and help you shape the future of technology and creativity.

  • This training course is all about making computers creative, just like we humans are. It's about teaching computers how to make art, music and stories, just like we are. You will learn how to use specialized computer languages, like Python, and AI software that boosts computer creativity. You will also learn how generative AI can be used in fields as diverse as art, music, games, and creating new products. It can help solve real-world problems, like designing better buildings or helping doctors diagnose diseases more quickly. Masters in Generative AI Training Prepares You for the Future. With more and more jobs requiring AI skills, mastering this field will not only help yourself, but it will also help shape the way we use technology in the coming years.

Exciting careers await you on completion of our course in various sectors such as manufacturing, IT, healthcare, telecommunications, etc. With the growing demand for AI in the industry, you can expect a significant salary increase of up to 200%. On an average, graduates earn around INR 6 million per year, but with hard work and dedication, higher salaries of up to INR 12 million are achievable. Our recruitment partners include renowned companies such as Accenture, Dell, Infosys, Adobe, etc. These partnerships provide numerous opportunities for graduates to advance their careers and excel in the field of AI. By collaborating with renowned companies, we offer our graduates optimal opportunities for success in professional life.

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Tools Covered of Masters in Generative AI

Power BI
Deep Learning
Machen Learning
Artificial Intelligence
Chat GPT
Prompt Engineering

Masters in Generative AI Curriculum

Course 1
Python for AI

Introduction To Python
Python Keyword and Identiers
Introduction To Variables
Python Data Type
Control Structure & Flow
Python Function, Modules and Packages
Decorator, Generator and Iterator
Python Exception Handling
Python File Handling
Memory management using python
Python Database Interaction
Reading an excel
Complete Understanding of OS Module of Python


Course Content

    • Installation and Working with Python
    • Understanding Python variables
    • Python basic Operators
    • Understanding the Python blocks.
    • Python Comments, Multiline Comments.
    • Python Indentation
    • Understating the concepts of Operators
    • Arithmetic
    • Relational
    • Logical
    • Assignment
    • Membership
    • Identity
    • Variables, expression condition and function
    • Global and Local Variables in Python
    • Packing and Unpacking Arguments
    • Type Casting in Python
    • Byte objects vs. string in Python
    • Variable Scope
    • Declaring and using Numeric data types
    • Using string data type and string operations
    • Understanding Non-numeric data types
    • Understanding the concept of Casting and Boolean.
    • Strings
    • List
    • Tuples
    • Dictionary
    • Sets
    • Statements if, else, elif
    • How to use nested IF and Else in Python
    • Loops
    • Loops and Control Statements.
    • Jumping Statements Break, Continue, pass
    • Looping techniques in Python
    • How to use Range function in Loop
    • Programs for printing Patterns in Python
    • How to use if and else with Loop
    • Use of Switch Function in Loop
    • Elegant way of Python Iteration
    • Generator in Python
    • How to use nested Loop in Python
    • Use If and Else in for and While Loop
    • Examples of Looping with Break and Continue Statement
    • How to use IN or NOT IN keyword in Python Loop.
    • What is List.
    • List Creation
    • List Length
    • List Append
    • List Insert
    • List Remove
    • List Append & Extend using + and Keyword
    • List Delete
    • List related Keyword in Python
    • List Reverse
    • List Sorting
    • List having Multiple Reference
    • String Split to create a List
    • List Indexing
    • List Slicing
    • List count and Looping
    • List Comprehension and Nested Comprehension
    • What is Tuple
    • Tuple Creation
    • Accessing Elements in Tuple
    • Changing a Tuple
    • Tuple Deletion
    • Tuple Count
    • Tuple Index
    • Tuple Membership
    • TupleBuilt in Function (Length, Sort)
    • Dict Creation
    • Dict Access (Accessing Dict Values)
    • Dict Get Method
    • Dict Add or Modify Elements
    • Dict Copy
    • Dict From Keys.
    • Dict Items
    • Dict Keys (Updating, Removing and Iterating)
    • Dict Values
    • Dict Comprehension
    • Default Dictionaries
    • Ordered Dictionaries
    • Looping Dictionaries
    • Dict useful methods (Pop, Pop Item, Str , Update etc.)
    • What is Set
    • Set Creation
    • Add element to a Set
    • Remove elements from a Set
    • PythonSet Operations
    • Frozen Sets
    • What is Set
    • Set Creation
    • Add element to a Set
    • Remove elements from a Set
    • PythonSet Operations
    • Python Syntax
    • Function Call
    • Return Statement
    • Arguments in a function Required, Default, Positional, Variable-length
    • Write an Empty Function in Python pass statement.
    • Lamda/ Anonymous Function
    • *args and **kwargs
    • Help function in Python
    • Scope and Life Time of Variable in Python Function
    • Nested Loop in Python Function
    • Recursive Function and Its Advantage and Disadvantage
    • Organizing python codes using functions
    • Organizing python projects into modules
    • Importing own module as well as external modules
    • Understanding Packages
    • Random functions in python
    • Programming using functions, modules & external packages
    • Map, Filter and Reduce function with Lambda Function
    • More example of Python Function
    • Creation and working of decorator
    • Idea and practical example of generator, use of generator
    • Concept and working of Iterator
    • Python Errors and Built-in-Exceptions
    • Exception handing Try, Except and Finally
    • Catching Exceptions in Python
    • Catching Specic Exception in Python
    • Raising Exception
    • Try and Finally
    • Opening a File
    • Python File Modes
    • Closing File
    • Writing to a File
    • Reading from a File
    • Renaming and Deleting Files in Python
    • Python Directory and File Management
    • List Directories and Files
    • Making New Directory
    • Changing Directory
    • Threading, Multi-threading
    • Memory management concept of python
    • working of Multi tasking system
    • Different os function with thread
    • SQL Database connection using
    • Creating and searching tables
    • Reading and Storing cong information on database
    • Programming using database connections
    • Working With Excel
    • Reading an excel le using Python
    • Writing to an excel sheet using Python
    • Python| Reading an excel le
    • Python | Writing an excel le
    • Adjusting Rows and Column using Python
    • ArithmeticOperation in Excel le.
    • Play with Workbook, Sheets and Cells in Excel using Python
    • Creating and Removing Sheets
    • Formatting the Excel File Data
    • More example of Python Function
    • Check Dirs. (exist or not)
    • How to split path and extension
    • How to get user prole detail
    • Get the path of Desktop, Documents, Downloads etc.
    • Handle the File System Organization using OS
    • How to get any les and folders details using OS
Course 2
Data Analysis and Visualization

Data Analysis and Visualization using Pandas.
Data Analysis and Visualization using NumPy and MatPlotLib
Introduction to Data Visualization with Seaborn


Course Content

    • Statistics
      • Categorical Data
      • Numerical Data
      • Mean
      • Median
      • Mode
      • Outliers
      • Range
      • Interquartile range
      • Correlation
      • Standard Deviation
      • Variance
      • Box plot
    • Pandas
      • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
      • How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
      • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalues
      • Exploring the Data Plotting, Correlations, and Histograms
      • Getting statistical information about the data Analysis Concepts, Handle the None Values
      • Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
      • Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
      • Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
      • Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
      • Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
      • Exporting the results to Excel Joins
      • Python | Pandas Data Frame Inner Join
      • Under sting the properties of Data Frame Left Join (Left Outer Join)
      • Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
      • Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
      • Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
      • Pandas | How to Group Data How to apply Lambda / Function on Data Frame
      • Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
    • NumPy
      • Introduction to NumPy Numerical Python
      • Importing NumPy and Its Properties
      • NumPy Arrays
      • Creating an Array from a CSV
      • Operations an Array from a CSV
      • Operations with NumPy Arrays
      • Two-Dimensional Array
      • Selecting Elements from 1-D Array
      • Selecting Elements from 2-D Array
      • Logical Operation with Arrays
      • Indexing NumPy elements using conditionals
      • NumPys Mean and Axis
      • NumPys Mode, Median and Sum Function
      • NumPys Sort Function and More
    • MatPlotLib
      • Bar Chart using Python MatPlotLib
      • Column Chart using Python MatPlotLib
      • Pie Chart using Python MatPlotLib
      • Area Chart using Python MatPlotLib
      • Scatter Plot Chart using Python MatPlotLib
      • Play with Charts Properties Using MatPlotLib
      • Export the Chart as Image
      • Understanding plt. subplots () notation
      • Legend Alignment of Chart using MatPlotLib
      • Create Charts as Image
      • Other Useful Properties of Charts.
      • Complete Understanding of Histograms
      • Plotting Different Charts, Labels, and Labels Alignment etc.
    • Introduction to Seaborn
      • Introduction to Seaborn
      • Making a scatter plot with lists
      • Making a count plot with a list
      • Using Pandas with seaborn
      • Tidy vs Untidy data
      • Making a count plot with a Dataframe
      • Adding a third variable with hue
      • Hue and scattera plots
      • Hue and count plots
    • Visualizing Two Quantitative Variables
      • Introduction to relational plots and subplots
      • Creating subplots with col and row
      • Customizing scatters plots
      • Changing the size of scatter plot points
      • Changing the style of scatter plot points
      • Introduction to line plots
      • Interpreting line plots
      • Visualizing standard deviation with line plots
      • Plotting subgroups in line plots
    • Visualizing a Categorical and a Quantitative Variable
      • Current plots and bar plots
      • Count plots
      • Bar plot with percentages
      • Customizing bar plots
      • Box plots
      • Create and interpret a box plot
      • Omitting outliers
      • Adjusting the whisk
      • Point plots
      • Customizing points plots
      • Point plot with subgroups
    • Customizing Seaborn Plots
      • Changing plot style and colour
      • Changing style and palette
      • Changing the scale
      • Using a custom palette
      • Adding titles and labels Part 1
      • Face Grids vs. Axes
      • Adding a title to a face Grid object
      • Adding title and labels Part 2
      • Adding a title and axis labels
      • Rotating x-tics labels and subplot
      • Putting it all together
      • Box plot with subgroups
      • Bar plot with subgroups and subplots
      • LM plot and heatmap
Course 3
Statistics for Data Science

Introduction to Statistics
EDA (Exploratory Data Analysis)
Data Pre-Processing & Data Mining
Introduction to Predictive Modelling


Course Content

    • Descriptive Statistics
    • Sample vs Population Statistics
    • Random variables
    • Probability distribution functions
    • Expected value
    • Normal distribution
    • Gaussian distribution
    • Z-score
    • Spread and Dispersion
    • Correlation and Co-variance
    • Need for structured exploratory data
    • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
    • Identify missing data
    • Identify outliers data
    • Imbalanced Data Techniques
    • Data Preparation
    • Feature Engineering
    • Feature Scaling, Feature Transformation and Dimensionality Reduction
    • Datasets
    • Dimensionality Reduction (PCA, ICA,LDA)
    • Anomaly Detection
    • Parameter Estimation
    • Data and Knowledge
    • Selected Applications in Data Mining
    • Difference between Analysis and Analytics
    • Concept of model in analytics and how it is used
    • Common terminology used in Analytics & Modelling process
    • Popular Modelling algorithms, Data Analytics Life cycle
    • Types of Business problems - Mapping of Techniques
    • Introduction to Machine Learning
Course 4
Databases ? MS SQL and SQL Queries

SQL Server Fundamentals
SQL Server 2019 Database Design
SQL Tables in MS SQL Server
Data Validation and Constraints
Views and Row Data Security
Indexes and Query tuning
Stored Procedures and Benets
System functions and Usage
Triggers, cursors, memory limitations
Cursors and Memory Limitations
Transactions Management


Course Content

    • SQL Server 2019 Installation
    • Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
    • SQL Server Features & Purpose
    • Using Management Studio (SSMS)
    • Conguration Tools & SQLCMD
    • Conventions & Collation
    • SQL Database Architecture
    • Database Creation using GUI
    • Database Creation using T-SQL scripts
    • DB Design using Files and File Groups
    • File locations and Size parameters
    • Database Structure modications
    • SQL Server Database Tables
    • Table creation using T-SQL Scripts
    • Naming Conventions for Columns
    • Single Row and Multi-Row Inserts
    • Table Aliases
    • Column Aliases & Usage
    • Table creation using Schemas
    • Basic INSERT
    • UPDATE
    • DELETE
    • SELECT queries and Schemas
    • Use of WHERE, IN and BETWEEN
    • Variants of SELECT statement
    • ORDER BY
    • HAVING
    • ROWCOUNT and CUBE Functions
    • Table creation using Constraints
    • NULL and IDENTITY properties
    • UNIQUE KEY Constraint and NOT NULL
    • PRIMARY KEY Constraint & Usage
    • CHECK and DEFAULT Constraints
    • Naming Composite Primary Keys
    • Disabling Constraints & Other Options
    • Benets of Views in SQL Database
    • Views on Tables and Views
    • Issues with Views and ALTER TABLE
    • Common System Views and Metadata
    • Common Dynamic Management views
    • Working with JOINS inside views
    • Need for Indexes & Usage
    • Indexing Table & View Columns
    • Index SCAN and SEEK
    • INCLUDED Indexes & Usage
    • Materializing Views (storage level)
    • Composite Indexed Columns & Keys
    • Indexes and Table Constraints
    • Primary Keys & Non-Clustered Indexes
    • Why to use Stored Procedures
    • Types of Stored Procedures
    • Use of Variables and parameters
    • INPUT and OUTPUT parameters
    • System level Stored Procedures
    • Dynamic SQL and parameterization
    • Scalar Valued Functions
    • Types of Table Valued Functions
    • System Functions and usage
    • Date Functions
    • Time Functions
    • String and Operational Functions
    • GROUPING Functions
    • Why to use Triggers
    • DML Triggers and Performance impact
    • INSERTED and DELETED memory tables
    • Data Audit operations & Sampling
    • Database Triggers and Server Triggers
    • Bulk Operations with Triggers
    • Cursor declaration and Life cycle
    • STATIC
    • SCROLL Cursors
    • FORWARD_ONLY and LOCAL Cursors
    • KEYSET Cursors with Complex SPs
    • ACID Properties and Scope
    • EXPLICIT Transaction types
    • IMPLICIT Transactions and options
    • AUTOCOMMIT Transaction and usage
Course 5
Analytics with Excel

Understanding Concepts of Excel
Ms Excel Advance
MIS Reporting & Dash Board
What is Macro
Recording a Macro
Different Components of a Macro
What is VBA and how to write macros in VBA.


Course Content

    • Creation of Excel Sheet Data
    • Range Name, Format Painter
    • Conditional Formatting, Wrap Text, Merge & Centre
    • Sort, Filter, Advance Filter
    • Different type of Chart Creations
    • Auditing, (Trace Precedents, Trace Dependents)Print Area
    • Data Validations, Consolidate, Subtotal
    • What if Analysis (Data Table, Goal Seek, Scenario)
    • Solver, Freeze Panes
    • Various Simple Functions in Excel(Sum, Average, Max, Min)
    • Real Life Assignment work
    • Advance Data Sorting
    • Multi-level sorting
    • Restoring data to original order after performing sorting
    • Sort by icons
    • Sort by colours
    • Lookup Functions
      • Lookup
      • VLookup
      • HLookup
    • Subtotal, Multi-Level Subtotal
    • Grouping Features
      • Column Wise
      • Row Wise
    • Consolidation With Several Worksheets
    • Filter
      • Auto Filter
      • Advance Filter
    • Printing of Raw & Column Heading on Each Page
    • Workbook Protection and Worksheet Protection
    • Specified Range Protection in Worksheet
    • Excel Data Analysis
      • Goal Seek
      • Scenario Manager
    • Data Table
      • Advance use of Data Tables in Excel
      • Reporting and Information Representation
    • Pivot Table
      • Pivot Chat
      • Slicer with Pivot Table & Chart
    • Generating MIS Report In Excel
      • Advance Functions of Excel
      • Math & Trig Functions
    • Text Functions
    • Lookup & Reference Function
    • Logical Functions & Date and Time Functions
    • Database Functions
    • Statistical Functions
    • Financial Functions
    • Functions for Calculation Depreciation
    • Dashboard Background
    • Dashboard Elements
    • Interactive Dashboards
    • Type of Reporting In India
      • Industry Related Dashboard
      • Indian Print Media Reporting
    • Understanding Macros
    • Recording a Macro
    • User Form
    • Title
    • Module
    • Writing a simple macro
    • Apply arithmetic operations on two cells using macros.
    • How to align the text using macros.
    • How to change the background color of the cells using macros.
    • How to change the border color and style of the cells using macros.
    • Use cell referencing using macros.
    • How to copy the data from one cell and paste it into another.
    • How to change the font color of the text in a cellusingmacros
Course 6
Analytics Microsoft Power BI

Introduction to Power BI
Power BI Desktop
Power BI Data Transformation
Modelling with Power BI
Data Analysis Expressions (DAX)
Power BI Desktop Visualisations
Introduction to Power BI Dashboard and Data Insights
Direct Connectivity
Publishing and Sharing
Refreshing Datasets


Course Content

    • Overview of BI concepts
    • Why we need BI
    • Introduction to SSBI
    • SSBI Tools
    • Why Power BI
    • What is Power BI
    • Building Blocks of Power BI
    • Getting started with Power BI Desktop
    • Get Power BI Tools
    • Introduction to Tools and Terminology
    • Dashboard in Minutes
    • Interacting with your Dashboards
    • Sharing Dashboards and Reports
    • Power BI Desktop
    • Extracting data from various sources
    • Workspaces in Power BI
    • Data Transformation
    • Query Editor
    • Connecting Power BI Desktop to our Data Sources
    • Editing Rows
    • Understanding Append Queries
    • Editing Columns
    • Replacing Values
    • Formatting Data
    • Pivoting and Unpivoting Columns
    • Splitting Columns
    • Creating a New Group for our Queries
    • Introducing the Star Schema
    • Duplicating and Referencing Queries
    • Creating the Dimension Tables
    • Entering Data Manually
    • Merging Queries
    • Finishing the Dimension Table
    • Introducing the another DimensionTable
    • Creating an Index Column
    • Duplicating Columns and Extracting Information
    • Creating Conditional Columns
    • Creating the FACT Table
    • Performing Basic Mathematical Operations
    • Improving Performance and Loading Data into the Data Model
    • Introduction to Modelling
    • Modelling Data
    • Manage Data Relationship
    • Optimize Data Models
    • Cardinality and Cross Filtering
    • Default Summarization & Sort by
    • Creating Calculated Columns
    • Creating Measures & Quick Measures
    • What is DAX
    • Data Types in DAX
    • Calculation Types
    • Syntax, Functions, Context Options
    • DAX Functions
    • Date and Time
    • Time Intelligence
    • Information
    • Logical
    • Mathematical
    • Statistical
    • Text and Aggregate
    • Measures in DAX
    • Measures and Calculated Columns
    • ROW Context and Filter Context in DAX
    • Operators in DAX - Real-time Usage
    • Quick Measures in DAX - Auto validations
    • In-Memory Processing DAX Performance
    • How to use Visual in Power BI
    • What Are Custom Visuals
    • Creating Visualisations and Colour Formatting
    • Setting Sort Order
    • Scatter & Bubble Charts & Play Axis
    • Tooltips and Slicers, Timeline Slicers & Sync Slicers
    • Cross Filtering and Highlighting
    • Visual, Page and Report Level Filters
    • Drill Down/Up
    • Hierarchies and Reference/Constant Lines
    • Tables, Matrices & Conditional Formatting
    • KPI's, Cards & Gauges
    • Map Visualizations
    • Custom Visuals
    • Managing and Arranging
    • Drill through and Custom Report Themes
    • Grouping and Binning and Selection Pane, Bookmarks & Buttons
    • Data Binding and Power BI Report Server
    • Why Dashboard and Dashboard vs Reports
    • Creating Dashboards
    • Conguring a Dashboard Dashboard Tiles, Pinning Tiles
    • Power BI Q&A
    • Quick Insights in Power BI
    • Custom Data Gateways
    • Exploring live connections to data with Power BI
    • Connecting directly to SQL Server
    • Connectivity with CSV & Text Files
    • Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel
    • Content packs
    • Update content packs
    • Introduction and Sharing Options Overview
    • Publish from Power BI Desktop and Publish to Web
    • Share Dashboard with Power BI Service
    • Workspaces (Power BI Pro) and Content Packs (Power BI Pro)
    • Print or Save as PDF and Row Level Security (Power BI Pro)
    • Export Data from a Visualization
    • Export to PowerPoint and Sharing Options Summary
    • Understanding Data Refresh
    • Personal Gateway (Power BI Pro and 64-bit Windows)
    • Replacing a Dataset and Troubleshooting Refreshing
Course 7
Mastering Machine Learning

Introduction to Machine Learning
Supervised Learning
Regression Algorithm
Classification Algorithm
Optimization Algorithm
Dimensionality Reduction
Unsupervised Learning
Association Rules Mining and Recommendation Systems
Reinforcement Learning
Time Series Analysis
Model Selection and Boosting


Course Content

    • What is Machine Learning
    • Machine Learning Use-Cases
    • Machine Learning Process Flow
    • Machine Learning Categories
    • Classification and Regression
    • Where we use classification model and where we use regression
    • Regression Algorithms and its types
    • Logistic Regression
    • Evaluation Matrix of Regression Algorithm
    • Implementing KNN
    • Implementing Nave Bayes Classifier
    • Implementation and Introduction to Decision Tree using CARTand ID3
    • Introduction to Ensemble Learning
    • Random Forest algorithm using bagging and boosting
    • Evaluation Matrix of classification algorithms (confusion matrix, r2score, Accuracy,f1-score,recall and precision
    • Hyperparameter Optimization
    • Grid Search vs. Random Search
    • Introduction to Dimensionality
    • Why Dimensionality Reduction
    • PCA
    • Factor Analysis
    • Scaling dimensional model
    • LDA
    • ICA
    • What is Clustering & its Use Cases
    • What is K-means Clustering
    • How does the K-means algorithm works
    • How to do optimal clustering
    • What is Hierarchical Clustering
    • How does Hierarchical Clustering work
    • What are Association Rules
    • Association Rule Parameters
    • Calculating Association Rule Parameters
    • Recommendation Engines
    • How do Recommendation Engines work
    • Collaborative Filtering
    • Content-Based Filtering
    • Association Algorithms
    • Implementation of Apriori Association Algorithm
    • What is Reinforcement Learning
    • Why Reinforcement Learning
    • Elements of Reinforcement Learning
    • Exploration vs. Exploitation dilemma
    • Epsilon Greedy Algorithm
    • Markov Decision Process (MDP)
    • Q values and V values
    • Q Learning
    • Values
    • What is Time Series Analysis
    • Importance of TSA
    • Components of TSA
    • What is Model Selection
    • Need for Model Selection
    • Cross Validation
    • What is Boosting
    • How do Boosting Algorithms work
    • Types of Boosting Algorithms
    • Adaptive Boosting
Course 8
Understanding Deep Learning for AI

Introduction to Text Mining and NLP
Extracting, Cleaning and Preprocessing Text
Analyzing Sentence Structure
Text Classification - I
Getting Started with TensorFlow 2.0
Introduction to Deep Learning
Neural Networks
Convolution Neural Network
Image Processing and Computer Vision
Regional CNN
Introduction to RNN and GRU
Faster Object Detection Algorithm
BERT Algorithm


Course Content

    • Overview of Text Mining
    • Need of Text Mining
    • Natural Language Processing (NLP) in Text Mining
    • Applications of Text Mining
    • OS Module
    • Reading, Writing to text and word files
    • Setting the NLTK Environment
    • Accessing the NLTK Corpora
    • Tokenization
    • Frequency Distribution
    • Different Types of Tokenizers
    • Bigrams, Trigrams & Ngrams
    • Stemming
    • Lemmatization
    • Stopwords
    • POS Tagging
    • Named Entity Recognition
    • Syntax Trees
    • Chunking
    • Chinking
    • Context Free Grammars (CFG)
    • Automating Text Paraphrasing
    • Machine Learning: Brush Up
    • Bag of Words
    • Count Vectorizer
    • Term Frequency (TF)
    • Inverse Document Frequency (IDF)
    • Introduction to TensorFlow 2.x
    • Installing TensorFlow 2.x
    • Defining Sequence model layers
    • Activation Function
    • Layer Types
    • Model Compilation
    • Model Optimizer
    • Model Loss Function
    • Model Training
    • Digit Classification using Simple Neural Network in TensorFlow 2.x
    • Improving the model
    • Adding Hidden Layer
    • Adding Dropout
    • Using Adam Optimizer
    • What is Deep Learning
    • Curse of Dimensionality
    • Machine Learning vs. Deep Learning
    • Use cases of Deep Learning
    • Human Brain vs. Neural Network
    • What is Perceptron
    • Learning Rate
    • Epoch
    • Batch Size
    • Activation Function
    • Single Layer Perceptron
    • What is NN
    • Types of NN
    • Creation of simple neural network using tensorflow
    • Image Classification Example
    • What is Convolution
    • Convolutional Layer Network
    • Convolutional Layer
    • Filtering
    • ReLU Layer
    • Pooling
    • Data Flattening
    • Fully Connected Layer
    • Predicting a cat or a dog
    • Saving and Loading a Model
    • Face Detection using OpenCV
    • Introduction to Vision
    • Importance of Image Processing
    • Image Processing Challenges Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories
    • Introduction to Image Image Transformation, Image Processing Operations & Simple Point Operations
    • Noise Reduction Moving Average & 2D Moving Average
    • Image Filtering Linear & Gaussian Filtering
    • Disadvantage of Correlation Filter
    • Introduction to Convolution
    • Boundary Effects Zero, Wrap, Clamp & Mirror
    • Image Sharpening
    • Template Matching
    • Edge Detection Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector
    • Effect of Noise
    • Laplacian Filter
    • Smoothing with Gaussian
    • LOG Filter Blob Detection
    • Noise Reduction using Salt & Pepper Noise using Gaussian Filter
    • Nonlinear Filters
    • Bilateral Filters
    • Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding
    • Image Sampling & Interpolation Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling
    • Image Interpolation Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation
    • Introduction to the dnn module
      • Deep Learning Deployment Toolkit
      • Use of DLDT with OpenCV4.0
    • OpenVINO Toolkit
      • Introduction
      • Model Optimization of pre-trained models
      • Inference Engine and Deployment process
    • Regional-CNN
    • Selective Search Algorithm
    • Bounding Box Regression
    • SVM in RCNN
    • Pre-trained Model
    • Model Accuracy
    • Model Inference Time
    • Model Size Comparison
    • Transfer Learning
    • Object Detection Evaluation
    • mAP
    • IoU
    • RCNN Speed Bottleneck
    • Fast R-CNN
    • RoI Pooling
    • Fast R-CNN Speed Bottleneck
    • Faster R-CNN
    • Feature Pyramid Network (FPN)
    • Regional Proposal Network (RPN)
    • Mask R-CNN
    • Issues with Feed Forward Network
    • Recurrent Neural Network (RNN)
    • Architecture of RNN
    • Calculation in RNN
    • Backpropagation and Loss calculation
    • Applications of RNN
    • Vanishing Gradient
    • Exploding Gradient
    • What is GRU
    • Components of GRU
    • Update gate
    • Reset gate
    • Current memory content
    • Final memory at current time step
    • What is LSTM
    • Structure of LSTM
    • Forget Gate
    • Input Gate
    • Output Gate
    • LSTM architecture
    • Types of Sequence-Based Model
    • Sequence Prediction
    • Sequence Classification
    • Sequence Generation
    • Types of LSTM
    • Vanilla LSTM
    • Stacked LSTM
    • CNN LSTM
    • Bidirectional LSTM
    • How to increase the efficiency of the model
    • Backpropagation through time
    • Workflow of BPTT
    • YOLO v3
    • YOLO v4
    • Darknet
    • OpenVINO
    • ONNX
    • Fast R-CNN
    • Faster R-CNN
    • Mask R-CNN
    • What is BERT
    • Brief on types of BERT
    • Applications of BERT
Course 9
Mastering Generative AI

Understanding ChatGPT
ChatGPT for Productivity
ChatGPT for Developers and Exploring ChatGPT API
Developing Web Application using ChatGPT
GPT models
Deep RNN and Deep LSTM
DBN Architecture
Generative Adversarial Networks GAN
Q-Learning Type of Reinforcement Learning
Speech to Text Building
Chatbot Building
Auto ML
Explainable AI
Generative AI Prompt Engineering and LLM


Course Content

    • Introduction to Generative AI
    • Introduction to ChatGPT and OpenAI
    • Unleashing the Power of ChatGPT
    • The Applications of ChatGPT
    • Human-AI Collaboration and the Future
    • Engaging with ChatGPT
    • Wrapping Up and Looking Ahead
    • Leveraging ChatGPT for Productivity
    • Mastering Excel through ChatGPT
    • Becoming a Data Scientist using ChatGPT
    • Data Analysis in PowerBI with ChatGPT
    • Creating a Content Marketing Plan
    • Social Media Marketing using ChatGPT
    • Keyword Search and SEO using ChatGPT
    • Generating Content using ChatGPT
    • Implementing ChatGPT for Customer Service
    • Email Marketing using ChatGPT
    • Developing a Project Management Plan using ChatGPT
    • ChatGPT for Creating Programs
    • ChatGPT for Debugging
    • ChatGPT for Integrating New Features
    • ChatGPT for Testing
    • Introducing OpenAI and ChatGPT API
    • Building web development architecture
    • Building backend server
    • Setting up the database
    • Setting up a React-based client-side application
    • Writing user API requests to MongoDB with Express and React
    • Fetching and updating the database with MongoDB API and routing with Express
    • Routing to React-based client-side application
    • Debugging and client-side coding
    • Building a BMI Calculation application
    • Building a website and create landing page content using ChatGPT
    • Transformers (Encoder - Decoder Model by doing away from RNN variants)
    • Bidirectional Encoder Representation from Transformer (BERT)
    • OpenAI GPT-2 & GPT-3 Models (Generative Pre-Training)
    • Text Summarization with T5
    • Configurations of BERT
    • Pre-Training the BERT Model
    • Introduction to LSTM Architecture
    • Importance of Cell State, Input Gate, Output Gate, Forget Gate, Sigmoid and Tanh
    • Mathematical Calculations to Process Data in LSTM
    • RNN vs LSTM - Bidirectional vs Deep Bidirectional RNN
    • Deep RNN vs Deep LSTM
    • Autoencoders
      • Intuition
      • Comparison with other Encoders (MP3 and JPEG)
      • Implementation in Keras
    • Deep AutoEncoders
      • Intuition
      • Implementing DAE in Keras
    • Convolutional Autoencoders
      • Intuition
      • Implementation in Keras
    • Variational Autoencoders
      • IntuitionImplementation in Keras
    • Introduction to Restricted Boltzmann Machines - Energy Function, Schematic implementation, Implementation in TensorFlow
    • Introduction to DBN
    • Architecture of DBN
    • Applications of DBN
    • DBN in Real World
    • Introduction to Generative Adversarial Networks (GANS)
    • Data Analysis and Pre-Processing
    • Building Model
    • Model Inputs and Hyperparameters
    • Model losses
    • Implementation of GANs
    • Defining the Generator and Discriminator
    • Generator Samples from Training
    • Model Optimizer
    • Discriminator and Generator Losses
    • Sampling from the Generator
    • Advanced Applications of GANS
      • Pix2pixHD
      • CycleGAN
      • StackGAN++ (Generation of photo-realistic images)
      • GANs for 3D data synthesis
      • Speech quality enhancement with SEGAN
    • Introduction to SRGAN
    • Network Architecture - Generator, Discriminator
    • Loss Function - Discriminator Loss & Generator Loss
    • Implementation of SRGAN in Keras
    • Reinforcement Learning
    • Deep Reinforcement Learning vs Atari Games
    • Maximizing Future Rewards
    • Policy vs Values Learning
    • Balancing Exploration With Exploitation
    • Experience Replay, or the Value of Experience
    • Q-Learning and Deep Q-Network as a Q-Function
    • Improving and Moving Beyond DQN
    • Keras Deep Q-Network
    • Speech Recognition Pipeline
    • Phonemes
    • Pre-Processing
    • Acoustic Model
    • Deep Learning Models
    • Decoding
    • Introduction to Chatbot
    • NLP Implementation in Chatbot
    • Integrating and implementing Neural Networks Chatbot
    • Introduction to Sequence to Sequence models and Attention
      • Transformers and it applications
      • Transformers language models
        • BERT
        • Transformer-XL (pretrained model: transfo-xl-wt103)
        • XLNet
    • Building a Retrieval Based Chatbot
    • Deploying Chatbot in Various Platforms
    • AutoML Methods
      • Meta-Learning
      • Hyperparameter Optimization
      • Neural Architecture Search
      • Network Architecture Search
    • AutoML Systems
      • MLBox
      • Auto-Net 1.0 & 2.0
      • Hyperas
    • AutoML on Cloud - AWS
      • Amazon SageMaker
      • Sagemaker Notebook Instance for Model Development, Training and Deployment
      • XG Boost Classification Model
      • Training Jobs
      • Hyperparameter Tuning Jobs
    • AutoML on Cloud - Azure
      • Workspace
      • Environment
      • Compute Instance
      • Compute Targets
      • Automatic Featurization
      • AutoML and ONNX
    • Introduction to XAI - Explainable Artificial Intelligence
    • Why do we need it
    • Levels of Explainability
      • Direct Explainability
        • Simulatability
        • Decomposability
        • Algorithmic Transparency
      • Post-hoc Explainability
        • Model-Agnostic Algorithms
        • Explanation by simplification (Local Interpretable Model-Agnostic Explanations (LIME))
        • Feature relevance explanation
        • SHAP
        • QII
        • SA
        • ASTRID
        • XAI
        • Visual Explanations
    • General AI vs Symbolic Al vs Deep Learning
    • Generative AI
      • Creative Applications
      • Data Augmentation
    • Diffusion Models
      • Realistic Data Generation
      • Applications Beyond Text
      • LLM
      • Prompt Engineering
        • Fine-Tuning for Specific Tasks
        • Mitigating Bias and Ethical Concerns
        • Tailoring to Domain-Specific Contexts
      • Building AI application with Gradio
      • Large language Model with Semantic Search
      • Pair Programming with Large Language Model
      • Understanding and Applying Text Embedding
      • LLMOps Vs DevOps
Course 10
Cloud: AWS(Amazon Web Services)

Introduction to Cloud Computing
Amazon EC2 and Amazon EBS
Amazon Storage Services S3 (Simple Storage Services)
Cloud Watch & SNS
Scaling and Load Distribution in AWS
Identity and Access Management Techniques (IAM)
Amazon Relational Database Service (RDS)
Multiple AWS Services and Managing the Resources' Lifecycle
AWS Architecture and Design
Migrating to Cloud & AWS
Router S3 DNS
Cloud Formation
Elastic Beanstalk
EFS / NFS (hands-on practice)
Hands-on practice on various Topics


Course Content

    • In this module, you will learn what Cloud Computing is and what are the different models of Cloud Computing along with the key differentiators of different models. We will also introduce you to virtual world of AWS along with AWS key vocabulary, services and concepts.
      • A Short history
      • Client Server Computing Concepts
      • Challenges with Distributed Computing
      • Introduction to Cloud Computing
      • Why Cloud Computing
      • Benefits of Cloud Computing
    • In this module, you will learn about the introduction to compute offering from AWS called EC2. We will cover different instance types and Amazon AMIs. A demo on launching an AWS EC2 instance, connect with an instance and host ing a website on AWS EC2 instance. We will also cover EBS storage Architecture (AWS persistent storage) and the concepts of AMI and snapshots.
      • Amazon EC2
      • EC2 Pricing
      • EC2 Type
      • Installation of Web server and manage like (Apache/ Nginx)
      • Demo of AMI Creation
      • Exercise
      • Hands on both Linux and Windows
    • In this module, you will learn how AWS provides various kinds of scalable storage services. In this module, we will cover different storage services like S3, Glacier, Versioning, and learn how to host a static website on AWS.
      • Versioning
      • Static website
      • Policy
      • Permission
      • Cross region Replication
      • AWS-CLI
      • Life cycle
      • Classes of Storage
      • AWS CloudFront
      • Real scenario Practical
      • Hands-on all above
    • In this module, you will learn how to monitoring AWS resources and setting up alerts and notifications for AWS resources and AWS usage billing with AWS CloudWatch and SNS.
      • Amazon Cloud Watch
      • SNS - Simple Notification Services
      • Cloud Watch with Agent
    • In this module, you will learn about 'Scaling' and 'Load distribution techniques' in AWS. This module also includes a demo of Load distribution & Scaling your resources horizontally based on time or activity.
      • Amazon Auto Scaling
      • Auto scaling policy with real scenario based
      • Type of Load Balancer
      • Hands on with scenario based
    • In this module, you will learn introduction to Amazon Virtual Private Cloud. We will cover how you can make public and private subnet with AWS VPC. A demo on creating VPC. We will also cover overview of AWS Route 53.
      • Amazon VPC with subnets
      • Gateways
      • Route Tables
      • Subnet
      • Cross region Peering
    • In this module, you will learn how to achieve distribution of access control with AWS using IAM.
    • Amazon IAM
      • add users to groups,
      • manage passwords,
      • log in with IAM-created users.
    • User
    • Group
    • Role
    • Policy
    • In this module, you will learn how to manage relational database service of AWS called RDS.
      • Amazon RDS
      • Type of RDS
      • RDS Failover
      • RDS Subnet
      • RDS Migration
      • Dynamo DB (No SQL DB)
      • Redshift Cluster
      • SQL workbench
      • JDBC / ODBC
    • In this module, you will get an overview of multiple AWS services. We will talk about how do you manage life cycle of AWS resources and follow the DevOps model in AWS. We will also talk about notification and email service of AWS along with Content Distribution Service in this module.
      • Cloud Trail,
    • In this module, you will cover various architecture and design aspects of AWS. We will also cover the cost planning and optimization techniques along with AWS security best practices, High Availability (HA) and Disaster Recovery (DR) in AWS.
      • AWS High Availability Design
      • AWS Best Practices (Cost +Security)
      • AWS Calculator & Consolidated Billing
    • Public DNS
    • Private DNS
    • Routing policy
    • Records
    • Register DNS
    • Work with third party DNS as well
    • Stack
    • Templet
    • Json / Ymal
    • Installation of Linux
    • Configuration
    • Manage
    • Installation of app on Linux (apache / Nginx etc)
    • AWS cli configuration on Linux
    • Complete hands-on on Linux.
    • Scenario based lab and practical
    • Each topic and services will be cover with lab and theory.
Course 11
Data Science - Live Projects


Course Content

CertificatesMaster's Program Certificate

You will get certificate after completion of program

Course Structure
  • - 9 Months Online Program
  • - 0+ Hours of Intensive Learning
  • - 0+ Assigments & 0+ Projects
  • - 0 Live Projects
Career Assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired

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Earn your certificate

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Industry Project


Real-life Case Studies

Work on case studies based on top industry frameworks and connect your learning with real-time industry solutions right away.


Best Industry-Practitioners

All of our trainers and highly experienced, passionate about teaching and worked in the similar space for more than 3 years.


Acquire essential Industrial Skills

Wisely structured course content to help you in acquiring all the required industrial skills and grow like a superstar in the IT marketplace.


Hands-on Practical Knowledge

Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.


Collaborative Learning

Take your career at the top with collaborative learning at the Croma Campus where you could learn and grow in groups.


Assignment & Quizzes

Practice different assignments and quizzes on different topics or at the end of each module to evaluate your skills and learning speed.

Placement & Recruitment Partners

Placement Assistance

We provide 100 percent placement assistance and most of our students are placed after completion of the training in top IT giants. We work on your resume, personality development, communication skills, soft-skills, along with the technical skills.

CAREERS AND SALARIES IN Masters in Generative AI

Master's in Generative AI opens up countless career opportunities, giving professionals a variety of opportunities to grow and excel. Here are some of the exciting career paths available to graduates:


As an AI Researcher, you'll dive deep into the development and advancements of generative AI technology. Your role will be to conduct research, experiment with new algorithms and push the boundaries of what's possible in the field. Salaries can range from INR 800,000 to INR 2,500,000 per annum.

In this role, you'll leverage your generative AI expertise to develop innovative solutions for creative industries like games, animation, and digital art. You'll work on a wide range of projects from generating realistic graphics to creating interactive storytelling experiences. Salary between INR 600,000 to INR 1,800,000 annually.

As an AI Product Manager, you'll oversee the development and delivery of AI-powered products and services. With your background in generative AI, you will play a key role in defining product strategy, identifying market opportunities and ensuring the successful adoption of AI technology. Salaries ranging from INR 1,000,000 to INR 3,000,000 per year.

Generative AI capabilities are extremely valuable in the field of data science. Data scientists use their expertise to analyze large data sets, gain insights, and develop predictive models. Knowledge of generative AI techniques enables them to tackle complex problems and develop creative solutions. salaries in the range of INR 700,000 to INR 2,500,000 per annum.

Use generative AI to create personalized, immersive user experiences. As a UX/UI designer specializing in generative AI, you will use AI-driven design tools and techniques to create innovative interfaces that adapt to user behavior and preferences. They can earn between INR 500,000 to INR 1,500,000 per year.

With your expertise in generative AI, you can embark on the path to self-employment and start your own AI-focused company. The opportunities for entrepreneurial ventures in this field are endless, including developing AI-powered products, providing consulting services, and creating AI-driven content. Range of INR 1,000,000 to INR 10,000,000 or more annually.

Sharing your knowledge and expertise in generative AI can be extremely rewarding. As an AI instructor, you'll have the opportunity to mentor and mentor aspiring AI professionals, conduct workshops and trainings, and contribute to the growth and development of the next generation of AI talent. Entry level salary ranges INR 400,000 to INR 800,000 per year.


On the completion of the course, you may work in various domains like manufacturing, It, healthcare, telecom, and more. Also, most of the students get 200 percent hike after completing this course. The average you will get 6 lac p.a. and for a little more efforts you may acquire salary packages up to 12 lacs p.a.


Admission Process

date timeImportant Date & Time

You can apply for the master program online at our site. Mark the important date and time related to the program and stay in touch with our team to get the information about the program in detail.

enrollEnrollment Criteria

Once you submit your profile online, it will be reviewed by our expert team closely for the eligibility like graduation degree, basic programming skills, etc. Eligible candidates can move to the next step quickly.

finalFinal Enrollment Process

Eligible candidates have to appear for the online assessment based on your graduation and basic programming knowledge. Candidates who clear the exam will appear for the interview and finally they can join the program.

Get a chance to win a scholarship up to
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Frequently Asked Questions

This is a program that teaches basic skills for data analysis, including statistical techniques and languages.

A basic understanding of mathematics and a high school diploma are sufficient.

The training program can be completed in five months and it totally depends on the caliber.

Programming experience is beneficial but not always required. The training program starts with the basics.

Opportunities include roles such as data analyst, business analyst business or research analyst.

Yes, Croma Campus offers online as well as offline courses to ensure flexibility and accessibility.

Yes, organizations are increasingly dependent on data, which is creating a growing demand for qualified associates.

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  • 61,640+ Satisfied Learners
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