Masters in Artificial Intelligence

Master's Program 4.8 out of 5 rating votes 9874

Understand the fundamentals of artificial intelligence. Learn how to create powerful AI solutions.

INR 75000

Excluding GST

100% Placement


Ranked#2 among Top Full-Time

Masters in Artificial Intelligence in India- 2010-2024

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Start Date : 4th Mar 2024Duration : 8 Months
Format : Live Online /Self-Paced/Classroom
Future of Masters in Artificial Intelligence

₹6 LPA to ₹35 LPA

An artificial intelligence engineer can make around ₹5 LPA. On the other hand, an experienced AI engineer can make around ₹35 LPA.


Job Opportunities

As per the reports of the World Economic Forum, the artificial intelligence industry will create approximately 97 million jobs by the year 2025.


Future Analytics

The global artificial intelligence (AI) software market is forecast to grow rapidly in the coming years, reaching around 126 billion U.S. dollars by 2025.


Masters in Artificial Intelligence

4.8 out of 5 rating vote 9874

Understand the fundamentals of artificial intelligence. Learn how to create powerful AI solutions..

INR 75000 + GST
100% Placement Assistance

Program Overview

In this course, you will learn to use ML and DL's power to create robust AI solutions. Moreover, you will gain the skills that are essential for becoming a proficient artificial intelligence engineer. For example, you learn skills like ML, DL, neural networks, Python, etc. The main of this course is to help students acquire skills that are important for marketing themselves as AI engineers. After going through this program, you can easily get a job as a:

  • AI engineer
  • AI developer
  • Big data engineer
  • Business intelligence developer
  • Data scientist

Thanks to the growing influence of AI in every industry and sector, a lot of opportunities for growth and progression are emerging in the market for professionals who pursue their careers in the AI industry. This is why organizations today are always looking for artificial intelligence experts and don't shy away to pay a good amount of money to AI experts for their services. Thus, you can guarantee yourself a phenomenal and highly fulfilling career by pursuing your career in the AI industry.

  • Web_IconAs per the reports of the World Economic Forum, the AI industry will create approximately 97 million jobs worldwide by the year 2025.
  • branVarious opportunities are available for professionals who pursue their careers in the AI industry. For example, you can work as an AI engineer, AI developer, etc.
  • polyAccording to a survey, around 80% of retail executives will adopt artificial intelligence-powered automation solutions by the year 2027.
  • analyticsThe global artificial intelligence (AI) software market is forecast to grow rapidly in the coming years, reaching around 126 billion U.S. dollars by 2025.

There is a constant increase in the number of firms adopting AI solutions in their organization with each passing year. The AI training program will help students develop skills that are essential for becoming proficient AI engineers. Moreover, you will learn about the impact of artificial intelligence on various industries and sectors and what are the advantages of using AI-based solutions.

m IconThe project-based training will help students acquire skills essential for becoming competent AI engineers and getting placed in a renowned firm.

 m IconAfter completing the artificial intelligence training program, you can easily get a job as an AI engineer in a renowned firm with a salary package of ₹5,00,000-₹21,00,000 PA.

icon 3As per a survey, around 97 million job opportunities will be created in the AI industry worldwide in the coming years.

The aim of the artificial intelligence training program is to make students competent AI engineers by giving them quality education. Furthermore, you will learn to develop AI solutions for enhancing a firm's performance and increasing its profits. Things you will learn:

  • AI fundamentals
  • Python Statistics
  • Data Automation in AI
  • Data Analysis & Visualization
  • Databases – MySQL and SQL
  • Data Science Professional Program
  • Machine Learning

The main objective of artificial intelligence training is to give top-notch training to students that wish to make their career in the AI industry. The course is designed in such a way that a student can easily master all the concepts of AI. Moreover, the content of the course is developed in consultation with AI experts and keeping in mind the emerging demands of the artificial intelligence industry.

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Tools Covered of Masters in Artificial Intelligence

Power BI
Deep Learning
Machen Learning

Masters in Artificial Intelligence Curriculum

Course 1
Masters in Artificial intelligence

Artificial intelligence is a theory and development of computer systems that can perform tasks that normally require human intelligence. Speech recognition, decision-making, visual perception, for example, are features of human intelligence that artificial intelligence may possess.


Course Content

    • In this program you will learn:
      • Masters in Artificial intelligence
      • Python Statistics for Artificial intelligence
      • Data Automation in AI
      • Data Analysis & Visualization
      • Databases – MySQL and SQL
      • Data Science Professional Program
      • Machine Learning
      • Live Projects
Course 2
Python Statistics for Artificial intelligence

This module offers knowledge to introduce you to the basic principles based on statistical methods and procedures followed in data analysis. This course will help you to understand the work process involved with summarizing the data, data storage, visualizing the data results, and a hands-on approach with statistical analysis with python.


Course Content

    • Introduction To Python:
      • Installation and Working with Python
      • Understanding Python variables
      • Python basic Operators
      • Understanding the Python blocks.
    • Introduction To Variables:
      • 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
    • Python Data Type:
      • Declaring and usingNumeric data types
      • Using stringdata type and string operations
      • Understanding Non-numeric data types
      • Understanding the concept of Casting and Boolean.
      • Strings
      • List
      • Tuples
      • Dictionary
      • Sets
    • Introduction Keywords and Identifiers and Operators
      • Python Keyword and Identifiers
      • Python Comments, Multiline Comments.
      • Python Indentation
      • Understating the concepts of Operators
    • Data Structure
      • List
        • 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 Revers
        • 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
      • Dictionary
        • 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.)
    • Sets, Tuples and Looping Programming
      • Sets
        • What is Set
        • Set Creation
        • Add element to a Set
        • Remove elements from a Set
        • PythonSet Operations
        • Frozen Sets
      • Tuple
        • What is Tuple
        • Tuple Creation
        • Accessing Elements in Tuple
        • Changinga Tuple
        • TupleDeletion
        • Tuple Count
        • Tuple Index
        • TupleMembership
        • TupleBuilt in Function (Length, Sort)
      • Control Flow
        • Loops
        • Loops and Control Statements (Continue, Break and 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 IF and Else 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 Statements
        • How to use IN or NOTkeywordin Python Loop.
    • Exception and File Handling, Module, Function and Packages
      • Python Exception Handling
        • Python Errors and Built-in-Exceptions
        • Exception handing Try, Except and Finally
        • Catching Exceptions in Python
        • Catching Specific Exception in Python
        • Raising Exception
        • Try and Finally
      • Python File Handling
        • 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
      • Python Function, Modules and Packages
        • Python Syntax
        • Function Call
        • Return Statement
        • Write an Empty Function in Python –pass statement.
        • Lamda/ Anonymous Function
        • *argsand **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
        • Programming using functions, modules & external packages
        • Map, Filter and Reduce function with Lambda Function
        • More example of Python Function
Course 3
Data Automation in AI

Data automation is the process of updating data on your open data portal programmatically, rather than manually. Any data that is updated manually risks being delayed because it is one more task an individual has to do as part of the rest of their workload.


Course Content

    • Data Automation (Excel, SQL, PDF etc)
      • Python Object Oriented Programming—Oops
        • Concept of Class, Object and Instances
        • Constructor, Class attributes and Destructors
        • Real time use of class in live projects
        • Inheritance, Overlapping and Overloading operators
        • Adding and retrieving dynamic attributes of classes
        • Programming using Oops support
      • Python Database Interaction
        • SQL Database connection using
        • Creating and searching tables
        • Reading and Storing configinformation on database
        • Programming using database connections
      • Reading an excel
        • Reading an excel file usingPython
        • Writing toan excel sheet using Python
        • Python| Reading an excel file
        • Python | Writing an excel file
        • Adjusting Rows and Column using Python
        • ArithmeticOperation in Excel file.
        • Plotting Pie Charts
        • Plotting Area Charts
        • Plotting Bar or Column Charts using Python.
        • Plotting Doughnut Chartslusing Python.
        • Consolidationof Excel File using Python
        • Split of Excel File Using Python.
        • Play with Workbook, Sheets and Cells in Excel using Python
        • Creating and Removing Sheets
        • Formatting the Excel File Data
        • More example of Python Function
      • Working with PDF and MS Word using Python
        • Extracting Text from PDFs
        • Creating PDFs
        • Copy Pages
        • Split PDF
        • Combining pages from many PDFs
        • Rotating PDF’s Pages
      • Complete Understanding of OS Module of Python
        • Check Dirs. (exist or not)
        • How to split path and extension
        • How to get user profile detail
        • Get the path of Desktop, Documents, Downloads etc.
        • Handle the File System Organization using OS
        • How to get any files and folder’s details using OS
Course 4
Data Analysis & Visualization

Data Analysis is the process of bringing order and structure to collected data. It turns data into information teams can use. Data visualization is the process of putting data into a chart, graph, or other visual format that helps inform analysis and interpretation.


Course Content

    • Data Analysis & Visualization
      • Pandas
        • Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
        • How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
        • Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
        • Exploring the Data Plotting, Correlations, and Histograms
        • Getting statistical information about the data Analysis Concepts, Handle the None Values
        • Reading files 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 Aggregate Function
        • Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (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 DataFrameand 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 aCSV
        • 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
        • NumPy’sMean and Axis
        • NumPy’sMode, Median and Sum Function
        • NumPy’sSort 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 whiskers
        • 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 Subplots
        • Adding a title to a face Grid object
        • Adding title and labels: Part 2
        • Adding a title and axis labels
        • Rotating x-tics labels
        • Putting it all together
        • Box plot with subgroups
        • Bar plot with subgroups and subplots
Course 5
Databases – MySQL and SQL

This module will help you to explore the query language SQL and its integration with My SQL to query the database. This module will help you to understand SQL access through data and the method to update and manipulate the data stored in the database. You will learn basic and advance concepts of MY SQL with complete practical exposure.


Course Content

    • Python - MySQL
      • Introduction to MySQL
      • What is the MySQLdb
      • How do I Install MySQLdb
      • Connecting to the MYSQL
      • Selecting a database
      • Adding data to a table
      • Executing multiple queries
      • Exporting and Importing data tables.
      • SQL Functions
        • Single Row Functions
        • Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date
    • General Functions
      • Count, Average, Sum, Now etc.
    • Joining Tables
      • Obtaining data from Multiple Tables
      • Types of Joins (Inner Join, Left Join, Right Join & Full Join)
      • Sub-Queries Vs. Joins
    • Operators (Data using Group Function)
      • Distinct, Order by, Group by, Equal to etc.
    • Database Objects (Constraints & Views)
      • Not Null
      • Unique
      • Primary Key
      • Foreign Key
    • Structural & Functional Database Testing using TOAD Tool
      • SQL Basic
        • SQL Introduction
        • SQL Syntax
        • SQL Select
        • SQL Distinct
        • SQL Where
        • SQL And & Or
        • SQL Order By
        • SQL Insert
        • SQL Update
        • SQL Delete
      • SQL Advance
        • SQL Like
        • SQL Wildcards
        • SQL In
        • SQL Between
        • SQL Alias
        • SQL Joins
        • SQL Inner Join
        • SQL Left Join
        • SQL Right Join
        • SQL Full Join
        • SQL Union
      • SQL Functions
        • SQL Avg()
        • SQL Count()
        • SQL First()
        • SQL Last()
        • SQL Max()
        • SQL Min()
        • SQL Sum()
        • SQL Group By
Course 6
Data Science Professional Program

This course will help you to gain complete insights into the applied statistics, database systems, data preparation, and machine learning algorithms. The master in data science course will help you to gain a broad skill set to advance your career in respective fields such as data engineering, computer programming, and data architecture.


Course Content

    • Introduction to Data Science
      • What is Analytics & Data Science
      • Common Terms in Analytics
      • What is data
      • Classification of data
      • Relevance in industry and need of the hour
      • Types of problems and business objectives in various industries
      • How leading companies are harnessing the power of analytics
      • Critical success drivers
      • Overview of analytics tools & their popularity
      • Analytics Methodology & problem-solving framework
      • List of steps in Analytics projects
      • Identify the most appropriate solution design for the given problem statement
      • Project plan for Analytics project & key milestones based on effort estimates
      • Build Resource plan for analytics project
      • Why Python for data science
    • Accessing/Importing and Exporting Data
      • Importing Data from various sources (Csv, txt, excel, access etc)
      • Database Input (Connecting to database)
      • Viewing Data objects - sub setting, methods
      • Exporting Data to various formats
      • Important python modules: Pandas
    • Data Manipulation: Cleansing - Munging Using Python Modules
      • Cleansing Data with Python
      • Filling missing values using lambda function and concept of Skewness.
      • Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
      • Normalizing data
      • Feature Engineering
      • Feature Selection
      • Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
      • Label encoding/one hot encoding
    • Data Analysis: Visualization Using Python
      • Introduction exploratory data analysis
      • Descriptive statistics, Frequency Tables and summarization
      • Univariate Analysis (Distribution of data & Graphical Analysis)
      • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
      • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)
      • Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
    • Introduction to Statistics
      • Descriptive Statistics
      • Sample vs Population Statistics
      • Random variables
      • Probability distribution functions
      • Expected value
      • Normal distribution
      • Gaussian distribution
      • Z-score
      • Central limit theorem
      • Spread and Dispersion
      • Inferential Statistics-Sampling
      • Hypothesis testing
      • Z-stats vs T-stats
      • Type 1 & Type 2 error
      • Confidence Interval
      • ANOVA Test
      • Chi Square Test
      • T-test 1-Tail 2-Tail Test
      • Correlation and Co-variance
    • Introduction to Predictive Modelling
      • Concept of model in analytics and how it is used
      • Common terminology used in Analytics & Modelling process
      • Popular Modelling algorithms
      • Types of Business problems - Mapping of Techniques
      • Different Phases of Predictive Modelling
    • Data Exploration for Modelling
      • 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 Pre-Processing & Data Mining
      • Data Preparation
      • Feature Engineering
      • Feature Scaling
      • Datasets
      • Dimensionality Reduction
      • Anomaly Detection
      • Parameter Estimation
      • Data and Knowledge
      • Selected Applications in Data Mining
Course 7
Machine Learning

Machine learning courses help to understand the complete concepts behind the processing of Artificial intelligence and Computer science. With the Machine learning course, you will cover topics based on supervised and unsupervised learning along with the development of software and algorithms to extract predictions based on data.


Course Content

    • Introduction to Machine Learning
      • Artificial Intelligence
        • AI overview
        • Meaning, scope, and 3 stages of AI
        • Decoding AI
        • Features of AI
        • Applications of AI
        • Image recognition
        • Effect of AI on society
        • AI for industries
        • Overview of machine learning
        • ML and AI relationship
      • Machine Learning
      • Techniques of Machine Learning
      • Machine Learning Algorithms
      • Algorithmic models of Learning
      • Applications of Machine Learning
      • Large Scale Machine Learning
      • Computational Learning theory
      • Reinforcement Learning
    • Supervised Machine Learning
      • Supervised Learning
        • What is Supervised Learning
        • Algorithms in Supervised learning
        • Regression & Classification
        • Regression vs classification
        • Computation of correlation coefficient and Analysis
        • Multivariate Linear Regression Theory
        • Coefficient of determination (R2) and Adjusted R2
        • Model Misspecifications
        • Economic meaning of a Regression Model
        • Bivariate Analysis
        • Naive Bayes classifier, Model Training
        • ANOVA (Analysis of Variance)
      • Semi-supervised and Reinforcement Learning
      • Bias and variance Trade-off
      • Representation Learning
    • Regression
      • Regression and its Types
      • Logistic Regression
      • Linear Regression
      • Polynomial Regression
    • Classification
      • Meaning and Types of Classification
      • Nearest Neighbor Classifiers
      • K-nearest Neighbors
      • Probability and Bayes Theorem
      • Support Vector Machines
      • Naive Bayes
      • Decision Tree Classifier
      • Random Forest Classifier
    • Unsupervised Learning: Clustering
      • About Clustering
      • Clustering Algorithms
      • K-means Clustering
      • Hierarchical Clustering
      • Distribution Clustering
    • Model optimization and Boosting
      • Ensemble approach
      • K-fold cross validation
      • Grid search cross validation
      • Ada boost and XG Boost
    • Introduction to Deep Learning
      • What are the Limitations of Machine Learning
      • What is Deep Learning
      • Advantage of Deep Learning over Machine learning
      • Reasons to go for Deep Learning
      • Real-Life use cases of Deep Learning
    • Deep Learning Networks
      • What is Deep Learning Networks
      • Why Deep Learning Networks
      • How Deep Learning Works
      • Feature Extraction
      • Working of Deep Network
      • Training using Backpropagation
      • Variants of Gradient Descent
      • Types of Deep Networks
      • Feed forward neural networks (FNN)
      • Convolutional neural networks (CNN)
      • Recurrent Neural networks (RNN)
      • Generative Adversal Neural Networks (GAN)
      • Restrict Boltzman Machine (RBM)
    • Deep Learning with Keras
      • Define Keras
      • How to compose Models in Keras
      • Sequential Composition
      • Functional Composition
      • Predefined Neural Network Layers
      • What is Batch Normalization
      • Saving and Loading a model with Keras
      • Customizing the Training Process
      • Intuitively building networks with Keras
    • Convolutional Neural Networks (CNN)
      • Introduction to Convolutional Neural Networks
      • CNN Applications
      • Architecture of a Convolutional Neural Network
      • Convolution and Pooling layers in a CNN
      • Understanding and Visualizing CNN
      • Transfer Learning and Fine-tuning Convolutional Neural Networks
    • Recurrent Neural Network (RNN)
      • Intro to RNN Model
      • Application use cases of RNN
      • Modelling sequences
      • Training RNNs with Backpropagation
      • Long Short-Term Memory (LSTM)
      • Recursive Neural Tensor Network Theory
      • Recurrent Neural Network Model
      • Time Series Forecasting
    • Natural Language Processing
      • NLP with python
      • Bags of words
      • Stemming
      • Tokenization
      • Lemmatization
      • TF-IDF
      • Sentiment Analysis
      • Overview of Tensor Flow
    • What is Tensor Flow
      • Tensor Flow code-basics
      • Graph Visualization
      • Constants, Placeholders, Variables
      • Tensor flow Basic Operations
      • Linear Regression with Tensor Flow
      • Logistic Regression with Tensor Flow
      • K Nearest Neighbor algorithm with Tensor Flow
      • K-Means classifier with Tensor Flow
      • Random Forest classifier with Tensor Flow
    • Neural Networks Using Tensor Flow
      • Quick recap of Neural Networks
      • Activation Functions, hidden layers, hidden units
      • Illustrate & Training a Perceptron
      • Important Parameters of Perceptron
      • Understand limitations of A Single Layer Perceptron
      • Illustrate Multi-Layer Perceptron
      • Back-propagation – Learning Algorithm
      • Understand Back-propagation – Using Neural Network Example
      • TensorBoard
    • Introduction to Big Data Hadoop and Spark
      • What is Big Data
      • Big Data Customer Scenarios
      • Understanding BIG Data: Summary
      • Few Examples of BIG Data
      • Why BIG data is a BUZZ
      • How Hadoop Solves the Big Data Problem
      • What is Hadoop
      • Hadoop’s Key Characteristics
      • Hadoop Cluster and its Architecture
      • Hadoop: Different Cluster Modes
      • Why Spark is needed
      • What is Spark
      • How Spark differs from other frameworks
      • Spark at Yahoo!
    • BIG Data Analytics and why it’s a Need Now
      • What is BIG data Analytics
      • Why BIG Data Analytics is a ‘need’ now
      • BIG Data: The Solution
      • Implementing BIG Data Analytics – Different Approaches
    • Traditional Analytics vs. BIG Data Analytics
      • The Traditional Approach: Business Requirement Drives Solution Design
      • The BIG Data Approach: Information Sources drive Creative Discovery
      • Traditional and BIG Data Approaches
      • BIG Data Complements Traditional Enterprise Data Warehouse
      • Traditional Analytics Platform v/s BIG Data Analytics Platform
    • Big Data Technologies
      • Scala
        • What is Scala
        • Scala in other Frameworks
        • Introduction to Scala REPL
        • Basic Scala Operations
        • Variable Types in Scala
        • Control Structures in Scala
        • Understanding the constructor overloading,
        • Various abstract classes
        • The hierarchy types in Scala,
        • For-each loop, Functions and Procedures
        • Collections in Scala- Array
      • Spark
        • Overview to Spark
        • Spark installation, Spark configuration,
        • Spark Components & its Architecture
        • Spark Deployment Modes
        • Limitations of Map Reduce in Hadoop
        • Working with RDDs in Spark
        • Introduction to Spark Shell
        • Deploying Spark without Hadoop
        • Parallel Processing
        • Spark MLLib - Modelling Big Data with Spark
    • Apache Kafka and Flume
      • What is Kafka Why Kafka
      • Configuring Kafka Cluster
      • Kafka architecture
      • Producing and consuming messages
      • Operations, Kafka monitoring tool
      • Need of Apache Flume
      • What is Apache Flume
      • Understanding the architecture of Flume
      • Basic Flume Architecture
Course 8
Live Projects

The training offers complete career transitioning projects based on the current needs of the organization. These projects are guided by experts and help you to add more value to your profile. You will learn to initiate data science projects based on a high-level perspective helping you to understand and articulate the innovative solutions for topical real-time data science projects.


Course Content

    • Live Projects
      • Managing credit card Risks
      • Bank Loan default classification
      • YouTube Viewers prediction
      • Super store Analytics (E-commerce)
      • Buying and selling cars prediction (like OLX process)
      • Advanced House price prediction
      • Analytics on HR decisions
      • Survival of the fittest
      • Twitter Analysis
      • Flight price prediction
CertificatesMaster's Program Certificate

You will get certificate after completion of program

Course Structure
  • - 8 Months Online Program
  • - 100+ Hours of Intensive Learning
  • - 8+ Assigments & 2+ Projects
  • - 2 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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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 Artificial Intelligence

Demand for AI experts in the market is going to grow at a rapid rate in the coming years. As per World Economic Forum, around 97 million jobs will be created in the AI industry in the coming years. This is mainly because today, many firms are adopting AI solutions in their organizations for optimizing their processes and increasing their productivity. Besides this, since the demand for AI experts is enormous in the market, a competent AI expert or engineer can earn a good amount of money in exchange for his services. As per the data of the Glassdoor website, an artificial intelligence engineer can earn around ₹ 5,00,000 -₹35,00,000 LPA.


AI engineers are responsible for developing and testing models. They use ML algorithms and neural networks for developing AI models. They are also responsible for developing and managing the AI infrastructure of a firm. A competent engineer must have knowledge about R programming, C++, statistics, NLP, etc. On average, an AI engineer can make around ₹5,00,000-₹21,00,000 PA.

An AI developer is responsible for conducting statistical analysis and statistical tests. Moreover, he is responsible for developing DL systems and creating and managing ML programs. A competent AI developer must be an expert in Python and Java. Furthermore, he should be proficient in working with Azure ML Studio, Amazon ML, etc. On average, an AI developer can make around ₹2,00,000-₹13,00,000 PA.

A BI developer is a professional that uses data analytics to share useful business information with the decision-makers of a company. He is also responsible for developing BI tools for helping a firm in improving its research process. A business intelligence developer must be an expert in working with JavaScript, VB, C#, SQL, Power BI, Oracle BI, etc. On average, a business intelligence developer can earn around ₹4,00,000-₹13,00,000 PA.

A data scientist assists a firm in gathering data from different sources for processing and assessing it to get useful business insights from it. These business insights help a firm in taking key decisions regarding the business and tackling various business issues. A data scientist must be proficient in Python, SQL, DL, ML, etc. On average, a data scientist can make around ₹6,00,000-₹17,00,000 PA.

An AI developer is responsible for conducting statistical analysis and statistical tests. Moreover, he is responsible for developing DL systems and creating and managing ML programs. A competent AI developer must be an expert in Python and Java. Furthermore, he should be proficient in working with Azure ML Studio, Amazon ML, etc. On average, an AI developer can make around ₹2,00,000-₹13,00,000 PA.


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
₹ 71,250 (Excluding of GST)

Frequently Asked Questions

  • Passion for learning
  • Go-getter attitude
  • Basic computer knowledge
  • Knowledge about AI fundamentals

Yes, you can join this AI training program even if you don't know anything about programming or coding?

  • Lots of job opportunities
  • Huge scope
  • AI is being used in every sector and industry
  • Enhances firm's productivity
  • AI solutions are highly affordable

  • ISO certified training institute
  • Project-based training
  • Industry recognized certification
  • Learn under an artificial intelligence expert

in 6 Months time period course will be completed

You can earn around ₹5,00,000-₹35,00,000 PA as an AI engineer.

If you like our Curriculum

What You will get Benefit
from this Program

  • Simulation Test Papers
  • Industry Case Studies
  • 61,640+ Satisfied Learners
  • 140+ Training Courses
  • 100% Certification Passing Rate
  • Live Instructor Online Training
  • 100% Placement Assistance
I’m interested in the program

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