Master Program in Machine Learning and Artificial Intelligence
Talk to our Course Advisor
Our Alumini Work At
About the course
Key Highlights
- Designed for Working Professionals
- 280+ Hours of Recorded Video Tutorials
- 20+ In-class & Capstone projects
- Live Classes- 5 days in a week & one day doubt clearing session
- 50+ Hours Career Assistance Videos
- Placement Assistance (Job Opportunities Portal, Hiring Drives, Resume Building & more)
- EMI Option Available
- Master Program Certification from IIBM Institute along with 3 additional Certificates
- Course Duration: 9 Months
- Eligibility: Any Bachelors degree with 50% or equivalent at graduation, No minimum work experience required
CURRICULUM HIGHLIGHTS
Introduction of SDLC | Need of a BA in SDLC | Phases of SDLC | “SDLC Methods | Waterfall Model and Agile, Scrum | Iterative and Incremental |V Model and Spiral Model” |”SDLC Pre-requisites and Activities | Common Criteria and Deliverables” | Software Maintenance lifecycle model |Software testing lifecycle model
What are requirements? And Characteristics of Requirements | Types of Requirements |Business Requirements | User Requirements and System Requirements | Functional Requirements and Non-Functional Requirements Implementation Requirement and UI Requirements
Initial Exploration | Form Business Requirements | Provide Solution to satisfy BusinessRequirements | Create Functional Specifications/Use Cases | Validate Requirements with Customer | Form SRS and Seek Approval | Requirements Framework
Sources of Requirement Elicitation | Skills for Requirement Elicitation | Stakeholder Identification | Surveys and Questionnaire | Interviewing and Focus Group Interviews | Brainstorming and Reverse Engineering | Prototyping and Wire Frames | JAD – Joint Application Development | Observation and Task Analysis | Domain Analysis and Persona Challenges in Requirement Elicitation
How to write Business Requirement document? | How to write Software Requirement specification | Introduction to Software Requirement Specification | Understanding SRS syntax with IEEE Standards | What are Use Case and Use Case Narrative? | Relationship between Use Cases | How to write Use Cases? | Use Case Narrative Flows – Primary Flow Alternative Flow, | Exceptional Flow|Activity Diagram,Class Diagram, E-R Model, Sequence Diagram, State Diagram, Collaboration Diagram
Business Process of existing system | GAP Analysis – PIECES Framework | Domain Properties and Stakeholders | Feasibility Study | Evaluation of Alternatives using Cost –Benefits Analysis
Use case Description and Use Case Diagram | Activity Diagram | What are Use Case and Use Case Narrative? |Relationship between Use Cases | How to write Use Cases? | Use Case Narrative Flows – Primary Flow, Alternative Flow, Exceptional Flow | Pre-condition, Post-condition, Exception handling and Triggers
Sequence Diagram | Class Diagram | Software Requirement Specification
RTM – Requirements Traceability Matrix | Requirements Change Management |Requirements Risk Management | Impact Analysis
Different dimensions of scope | Managing Scope at different stages of the Project Product Scope and Project Scope | Issues in scope management | Measurement of
Scope and Metrics
Steps in Risk Management | Risk Identification | Risk Analysis and Prioritization Risk Response – Strategy, Actions & Response Owners | Risk Monitoring and Control | Risk Management Documents
Introduction to Estimation | The Importance of Estimation | What is Estimation? | The Estimation Process Overview | Problems with Estimations | Estimation Techniques
Importance of CEM | Traditional and modern view | Understanding Customer and Managing Expectations | Issues in Customer Expectation Management | Handling
Di cult Situations | Expectation Management Life-Cycle
Quality Management System | Concept of Quality | Metrics and Measurements | Defect Prevention | Defect analysis tools and techniques
Communication: Introduction | Email Communication | Teleconference and Meetings | Assertiveness and Scenarios
Understanding IT project hierarchy | Project Charter and Requirements Process | RACI Matrix and Requirements Planning | Work Efforts & Estimations | Managing Requirements BA’s plan to feed into Project Plan
Define Prototyping and Importance of prototyping | Types of Prototyping | Prototyping as methodology | User Interface Prototyping |Advantage and Disadvantages of Prototyping
Business Requirement Document (BRD) | Use case document (USD) | Software Requirement Specification Document (SRS) | Change Request Process Document | Functional Requirement Specification (FSD) | Business Process Questionnaire Document Project Requirement Management and development process Document | Scope management Document | Requirement Traceability matrix document
Use Case Diagram and Class Diagram | Sequence Diagram and Collaboration Diagram |Activity Diagram and State Diagram
Rational Requisite Pro | Microsoft Visio – UML Tool | Team Foundation Server (TFS) |JIRA – Agile Tool | SVN – Configuration Management Tool | Axure – Prototype Tool
The Product Backlog Creation | High-level Project and Process Plan | Sprint Planning Meeting | The Sprint and Daily Scrum Meetings | Sprint Review Meeting | Sprint Retrospective | Next Sprint and Repeat | Post-Sprint Functional Testing by PO | PrereleaseTesting prior to Release to Customer | Release to Customer
Requirement Development Process – For New Development Project | Requirement Management Process – For Maintenance Project | Change Request (CR) Process
What is Project Management? | Project Management Phases | Project Management Knowledge Areas | Project Management Tools
BABOK Introduction | BABOK Knowledge Areas | Business Analysis Planning | Enterprise Analysis | Requirement Elicitation | Requirement Analysis | Solution Assessment and Validation | Requirement Management and Communication
Agile Perspectives | Business Intelligence Perspectives | Information Technology Perspectives | Business Architecture Perspectives | Business Process Management
Perspectives
What is R? And Why R?-Different “flavors” of R-Installing R Studio DesktopUnderstanding R Studio-Installing Packages and Libraries in R Studio-Setting Your Work Directory.
Data Variables-Data Types – Operators – Keywords – ExceptionsFunctions
Vectors and Lists – Strings and Matrices – Arrays and Factors – Data Frames – Packages.
R- CSV files Read and Write and analyze the data – R- Excel files Read and Write and analyze the data
Introduction to Visualisation – Line Plots and Bar Charts – Pie Chart and Histogram – Scatter Plots and Parallel Coordinates – Advanced Plotting – Exporting Plots and Other Plotting Packages
Linear Regression Analysis – Formulation of Regression Model – Bivariate Regression – Statistics Associated with Bivariate Regression Analysis – Conducting Bivariate Regression Analysis – Multiple Regressions – Conducting Multiple Regression – Mapping Bivariate Regression with Real Time Example.
Logistic Function – Single Predictor Model – Determine Logistic Cut off – Estimated Equation for Logistic Regression
Factor Analysis Introduction – Factor Analysis Model – Statistics associated with Factor Analysis – Conducting Factor Analysis – Construction of Factor Analysis – Factor Analysis Method – Principal Component Analysis – Rotation Method – Mapping Factor Analysis with Real Time Example
Cluster Analysis Introduction – Statistics associated with Cluster Analysis – Conducting Cluster Analysis – Classification of Clustering Procedure – Hierarchical Clustering – Non Hierarchical Clustering
Association Rule Introduction – Apriori Algorithm – Multiple Association Rules – Market Basket Analysis (MBA) – Application of Apriori Algorithm and Market Basket Analysis
Naïve Bayes Introduction – Probabilistic Basics and Probabilistic Classification – Characteristics of Naïve Bayes – Real Time Case study using Naïve Bayes – Advantage and Shortcoming of Naïve Bayes
K – Nearest Neighbour Introduction – K – Nearest Neighbour Algorithm – Pre-Processing your dataset for KNN – How to measure “Nearby” – Choosing “K” and High “K” vs. Low “K” – Real Time case study using KNN – Advantage and Disadvantage of KNN
What is a Decision Tree? – How to create Decision Tree – Choosing and Identifying attributes for Decision Tree – Entropy and Information Gain with Intuitions – Pruning Trees and its types – Forward Pruning and Backward Pruning – Sub tree Replacement and Raising – Real time case study with Decision Tree
Ensample of Decision Tree.
Linear SVM using Hyperplane – Non-Linear Hyperplane using Kernal Trick and Advantage and Disadvantage of SVM
RFM Segmentation and Analysis – Propensity Modelling and its application – Churn Modelling using Operational Analytics – Fundamentals and Modelling Framework – Industry application – Market Basket Analysis using Marketing Analytics – Fundamentals and Analysis Framework – Industry Application – Price and In store Promotion using Retail Analytics – Price Elasticity and Optimization – Promotion Effectives using Analytics
Introduction | RDBMS | Constraints | Normalisation | Syntax | Operators | Database querie | Table queries | Indexes | Handling | Duplicates | Indexes
Data Analytics across Domains | What is Analytics? | Types of Analytics | AI vs ML vs DL vs DS
Introduction to statistics and Central Limit Theorem | Measures of Central Tendancies and Measures of Spread | Descriptive Statistics with Real Time Examples | Measuring Scales | Inferential Statistics with Real Time Examples
Hypothesis Testing and Goodness of Fit test | Introduction to Statistical Tests | Statistical Test with Real Time Example | Analysis of Variance(ANOVA) & Analysis of Covariance (ANCOVA) | Probability Theory for Data Analytics | Types of Probability Distribution
Python Intro,IDE and Python Packages | Python Programming | Python Data Types – Dictionary, List and Set
Numpy Packages – Array Handling and Manupulation | Pandas Packages – Dataframe and Loading Excel, CSV File | Matplotlib Packages – Line graph and Visualisation
Histogram, Scatter Diagram, Box Plot and Bar Graph, Area Chart, Dual Axis, Array reshaping, reverse matrix, analysis
Python – Operators and String Manupulation | Control Structures(IF,IF-ELSE,IF-ELIF-ELSE, WHILE & FOR LOOP)
Python – Data Preparation Process | Python – Functions WITH and WITHOUT arguments Python – File Processing and Data Collection Methods
Python – Time Series Analysis and Forcasting | Python – Simple Predictive Analysis
Data Science with Python | Data Science Application across Multiple Domain and Business Function | Data Science Project LifeCycle
Multiple Predictive Model using Python | Python – Simple and Multiple Predictive Model in Practical | Python Correlation Analysis | Python Classifcation Model Building
Data Science – Experimental Design Analysis | Classifcation Technique – Discriminant Analysis | Data Science – Association Rule – Apriori Algorithm | Data Science – Building Recommendation System – (Market Basket Analysis)
Data Architecture Design, Data Warehousing and it’s Schema Design |Image Processing and Image Extraction | Image Processing and Object Recognition | Summarisation of Data Science Algorithm (Data Science Process)
Machine Learning Introduction
Linear Regression | Logistics Regression | ANOVA and ANCOVA | Linear Discriminant Analysis
Naïve Bayes | K-Nearest Neighbour | SVM- Support Vector Machine | -Decision Tree and Random Forest
Factor Analysis | Cluster Analysis | Association Rule | Correlation
Time Series Analysis
Deep Learning Fundamentals
Working of Neural Networks
Gradient Descent and Back Propagation
Activation Function
Tensorflow Introduction
Building Artificial Neural Networks (ANN)
Deep Learning-ANN-classification
Computer-Vision-opencv-part1-overview
Computer-Vision-opencv-part2-face_detection
intro to CNN
Introduction to RNN & Sequence prediction using RNN
Introduction to LSTM,
Sequence prediction using LSTM
Applications in text analytics , stock prediction , time series data
Basics of NLP | NLP- tolinisation | Removing Stop Words | Stemming & lemmatization | Parts of speech tagging | TFIDF vectorizer | Bag of words | Senmiment Analysis
Text Classification with Linear Models | Language Modelling with Probabilistic Graphical Models and Neural Networks | Word Embeddings and Topic Models | Machine Translation and Sequence-To-Sequence Models
NLP-Speech-Recognition-and-Text-to-Speech
Introduction to Reinforcement Learning | Model-Based Reinforcement Learning (Dynamic Programming)Model-Free Reinforcement Learning (SARSA, Monte Carlo, QLearning) | Approximate and Deep Reinforcement Learning (Deep Q- Learning) | Policy Gradient Reinforcement Learning | Advanced Topics on Exploration and Planning
Tableau introduction
Different types of visualization using Tableau
Tableau Dashboard Creation
Tableau Story line creation
Time series using Tableau
Different types of Joins Using Tableau
Tableau Features – Filters and format the Column
Real time project using Tableau
Tableau Highlighter
Data Blending using Tableau
Table Calculation using Tableau
Parameters and Set using Tableau
Advanced Data Preparation using Tableau
Hierarchical clustering using Tableau
Complete Course Revision using Tableau
| What is BigData |
| Characterstics of BigData |
| Problems with BigData |
| Handling BigData |
| Linux Commands |
| HDFS Commands |
| SQOOP ARCH and HANDSON: How Import data from Target RDBMS TO HDFS. USecase1: With Primary Key and Without Primary Key useCase2: Boundary Query Without columns and With Columns UseCase3: Incremenatl Load Usecase4: How to Import all tables at a time Usecase5: How to Import all Tables with Exclude Tables UseCase6: How to Create Sqoop Job UseCase7: How to Use $Conditions in Sqoop UseCase8: How to Import data from RDBMS to HIVE TABLE Usecase9: How to Process Semi Structured data using Sqoop Usecase10: Sqoop Export from HDFS to RDBMS |
| HIVE ARCH AND HANDSON: Different Types OF Tables In Hive PARTITIONING Different Types Of Partitioning Bucketing How to Perform Both Partitioning and Bucketing using one table Joins(Reducer Side Joins and MapSide Joins) How to Semi Structured Data using Hive Different File Format In Hive How to perform Updates and Deletes in Hive Hive Complex Types Hive UDf |
| HBASE ARCH AND HANDSON: Differnce Between Hive,SQL and HBASE How to create tables,insert,update and delete How to import data from rdbms to HBASE using Sqoop How to Load CSV DATA INTO HBASE TABLE HIVE to HBASE INTEGRATION |
| PIG AND MAPREDUCE |
| SCALA: What Is Scala Differnce between JAVA and SCALA SCala Variables For,While and Do while Loop Condiotional Statements String,String Methods,String Interpolation Functions Higher Order Functionss Anonymous Functions Closure Function Currying Function Collections(Array,set,tuple,map and list) File Handling Exception Handling Traits |
| Spark vs Map Reduce Architecture of Spark Spark Shell introduction Creating Spark Context Spark Project with Maven in Eclipse Cache and Persist in Spark File Operations in Spark RDD: What is RDD Transformations and Actions Loading data through RDD key-value pair RDD Pair RDD oeprations Running spark application with Spark-shell Deploying Application With Spark-Submit Spark-SQL: introduction to Spark SQL Hive vs SparkSQL Processing different fileformats using Spark SQL DataFrames DAG Lineage Graph Cluster types Optimizers Structured Streaming RDDs to Relations |
| Spark Streaming: introduction to Spark Streaming Architecture of spark Streaming SparkStreaming vs Flume introduction to Kafka Kafka Architecture Spark Streaming integration with Kafka Overview Real Time Examples |
Power BI Services | Advantages of Visual analytics
Installation Process of Power BI Desktop and Getting Familiar with Interface
Filters | Splitting Columns | Groups| Merging | Conditional Columns
Cardinality | Cross Filters | DAX Functions
Different Types of Visual Features | Drill Down | Formatting Visuals
How to Export Desktop Reports to Cloud Service and Explore my Workspace, Sharing with Others.
PROGRAMMING LANGUAGES AND TOOLS
Capstone Projects
Capstone Projects
Data Analytics
Bank Loan Modeling solution execution
Data Analytics
Application of Machine Learning Algorithm in Attrition Project and its analysis
Machine Learning
Attribution Analysis Solution Execution
Machine Learning
Bank Loan Modelling and its analysis
Machine Learning
Application of Machine Learning Algorithm in Bank Loan Modelling
Machine Learning
Merger and Acquisitions analytics
Machine Learning
LKP Project using Python
Machine Learning
Recommendation of new model
Artificial Intelligence
Telecom Churm case study using Sklearn
Artificial Intelligence
Recommendation Engine
Artificial Intelligence
Sentiment Analyser
Artificial Intelligence
Building Chatbot
Artificial Intelligence
Twitter Sentiment Analyser
Business Analysis
Online Recruitment Process
Data Visualization Tableau
Customer Loyalty Analytics and its Application
Data Visualization Tableau
Attrition Analysis and Bank Loan Modelling
Data Science using R
Solution- HR Analytics Attrition Analysis
Data Science using R
Merger and Acquisition
CERTIFICATION
On completion of the Master Program in Machine Learning and Artificial Intelligence, aspirants will receive an Industry-endorsed Certificate along with additional certificate.
PROGRAM FACULTY & TRAINERS
TRAINER
DINESH BABU-R
B.Tech and MBA ( Finance & Operation ), Ph.D in Data Analysis.
GANESH BHURE
Management LDP Program, B. Tech./B.E. (Electronics & Telecommunication)
SURYA
Master Of Computer Applications (M.C.A), B.Sc. (Electronics).
PLACEMENT MENTORS
ANOOP MATHEW
M.Tech (power electronics), MBA-HR, PhD in power quality improvement
DEVENDRA KUMAR
M.B.A(Marketing & Finance )
Program Fees
LUMPSUM Fees - Rs. 55,000 + GST 18% applicable
INSTALLMENT Fees - Rs. 60,000 + GST 18% applicable
| Regsitration Amount | EMI 1 | EMI 2 | EMI 3 | EMI 4 | EMI 5+18% GST |
|---|---|---|---|---|---|
| 10,000 | 15,000 | 15,000 | 15,000 | 5,000+6,000 | 10,800 |
- Exam Fees of 6,000/- applicable for complete course.
- EMI has to be paid every month. If not paid, fine of 500/- Rs. is applicable.
