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Advanced Certification in Data Science and AI

Master the skills of Machine Learning & Artificial Intelligence with this advanced Data Science and Artificial Intelligence by IIT Madras & Intellipaat. You will get to learn from the IIT Madras faculty & industry experts with 1:1 mentorship in this intensive online bootcamp.

Only Few Seats Left No Prior Programming Knowledge Required

Ranked #1 Data Science Program by India TV

Learning Format

Online Bootcamp

Live Classes

7 Months

Career Services

by Intellipaat

CCE IIT Madras

Certification

500+

Hiring Partners

About Program

This online Data Science and Artificial Intelligence advanced certification course led by the IIT Madras faculty aims at helping you master all the basic and advanced level skills that are crucial in the field of Data Science, Machine Learning, Deep Learning, and Artificial Intelligence.

Key Highlights

400 Hrs of Applied Learning
50+ Live sessions across 7 months
218 Hrs of Self-Paced Learning
Learn from IIT Madras Faculty & Industry Practitioners
50+ Industry Projects & Case Studies
One-on-One with Industry Mentors
Career Services by Intellipaat
3 Guaranteed Interviews by Intellipaat
24*7 Support
Designed for Working Professionals & Freshers
Soft Skills Essential Training
No Cost EMI Option
2 Days campus immersion at IIT Madras

Free Career Counselling

We are happy to help you 24/7

About IIT Madras Digital Skills Academy

IIT Madras Digital Skills Academy has initiated various programs in partnership with NASSCOM. The courses offered by them aim to upskill millions of students and professionals in trending technologies through a blend of theoretical and hands-on knowledge and are taught by leading academicians.

Upon Completion of this course, you will:

  • Receive an Advanced Certification in Data Science and AI from IIT Madras center for continuing education.
  • Receive live lectured from IIT Madras Faculty & Industry Experts

Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

400+ Hiring Partners

Career Transition Handbook

Who Can Apply for the Course?

  • Individuals with a bachelor’s degree and a keen interest to learn AI and Data Science
  • IT professionals looking for a career transition as Data Scientists and Artificial Intelligence Engineers
  • Professionals aiming to move ahead in their IT career
  • Artificial Intelligence and Business Intelligence professionals
  • Developers and Project Managers
  • Freshers who aspire to build their career in the field of Artificial Intelligence and Data Science
Who can aaply

What roles can a Data Science & Artificial Intelligence play?

Senior Data Scientist

Understand the issues and create models based on the data gathered, and also manage a team of Data Scientists.

AI Expert

Build strategies on frameworks and technologies to develop AI solutions and help the organization prosper.

Machine Learning Expert

With the help of several Machine Learning tools and technologies, build statistical models with huge chunks of business data.

Applied Scientist

Design and build Machine Learning models to derive intelligence for the numerous services and products offered by the organization.

Big Data Specialist

Create and manage pluggable service-based frameworks that are customized in order to import, cleanse, transform, and validate data.

Senior Business Analyst

Extract data from the respective sources to perform business analysis, and generate reports, dashboards, and metrics to monitor the company’s performance.

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Skills to Master

Python

Data Science

Data Analysis

AI

GIT

MLOps

Data Wrangling

SQL

Story Telling

Machine Learning

Prediction algorithms

NLP

PySpark

Model

Data visualization

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Tools to Master

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Meet Your Mentors

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.

Curriculum

Live Course Self Paced

Python 

  • Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding. 

Linux

  • Introduction to Linux  – Establishing the fundamental knowledge of how linux works and how you can begin with Linux OS. 
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux. 

Excel Fundamentals 

  • Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 
  • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 
  • VBA Macros, Ranges and Worksheet in VBA 
  • IF conditions, loops, Debugging, etc.

Excel For Data Analytics 

  • Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.  

Data Visualization with Excel

  • Charts, Pie charts, Scatter and bubble charts
  • Bar charts, Column charts, Line charts, Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools 

  • Power Pivot, Power Query and Power View

Classification Problems using Excel

  • Binary Classification Problems, Confusion Matrix, AUC and ROC curve 
  • Multiple Classification Problems  

Information Measure in Excel

  • Probability, Entropy, Dependence 
  • Mutual Information 

Regression Problems Using Excel

  • Standardization, Normalization, Probability Distributions 
  • Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
  • Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

Hands-on Exercise:

Classification problem using excel on sales data, and statistical tests on various samples from the population.

SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables 

Advanced SQL –  

  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

  • Types of UDFs, Inline table value, multi-statement table. 
  • Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions.

Hands-on exercise: 

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Version Control 

  • What is version control, types, SVN.

GIT 

  • Git Lifecycle, Common Git commands, Working with branches in Git
  • Github collaboration (pull request), Github Authentication (ssh and Http)
  • Merging branches, Resolving merge conflicts, Git workflow

Descriptive Statistics – 

  • Measure of central tendency, measure of spread, five points summary, etc. 

Probability 

  • Probability Distributions, bayes theorem, central limit theorem. 

Inferential Statistics –  

  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Extract Transform Load

  • Web Scraping, Interacting with APIs 

Data Handling with NumPy

  • NumPy Arrays, CRUD Operations,etc. 
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices. 

Data Manipulation Using Pandas

  • Loading the data, dataframes, series, CRUD operations, splitting the data, etc. 

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc. 
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross validation techniques, train-test split, etc. 

Data Visualization

  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc, with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn

Introduction to Machine learning 

  • Supervised, Unsupervised learning.
  • Introduction to scikit-learn, Keras, etc. 

Regression 

  • Introduction classification problems, Identification of a regression problem, dependent and independent variables. 
  • How to train the model in a regression problem. 
  • How to evaluate the model for a regression problem. 
  • How to optimize the efficiency of the regression model. 

Classification 

  • Introduction to classification problems, Identification of a classification problem, dependent and independent variables. 
  • How to train the model in a classification problem. 
  • How to evaluate the model for a classification problem. 
  • How to optimize the efficiency of the classification model. 

Clustering 

  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables. 
  • How to train the model in a clustering problem. 
  • How to evaluate the model for a clustering problem. 
  • How to optimize the efficiency of the clustering model.

Supervised Learning 

  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc. 
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc. 
  • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.   
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach. 
  • Support Vector Machine – SVM or support vector machines for regression and classification problems. 
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function. 
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems. 
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting. 

Unsupervised Learning 

  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach. 
  • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation. 
  • Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data. 
  • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Performance Metrics

  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc. 
  • Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model. 
  • r2, adjusted r2, mean squared error, etc.

Artificial Intelligence Basics 

  • Introduction to keras API and tensorflow

Neural Networks

  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks 

Deep Learning 

  • Deep neural networks
  • Convolutional Neural Networks 
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, restricted boltzmann machine 

Text Mining, Cleaning, and Pre-processing

  • Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

Text classification, NLTK, sentiment analysis, etc  

  • Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.

Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

  • Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

AI Chatbots and Recommendations Engine 

  • Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI. 

RBM and DBNs & Variational AutoEncoder

  • Introduction rbm and autoencoders
  • Deploying rbm for deep neural networks, using rbm for collaborative filtering
  • Autoencoders features and applications of autoencoders.

Object Detection using Convolutional Neural Net

  • Constructing a convolutional neural network using TensorFlow
  • Convolutional, dense, and pooling layers of CNNs
  • Filtering images based on user queries

Generating images with Neural Style and Working with Deep Generative Models

  • Automated conversation bots leveraging
  • Generative model, and the sequence to sequence model (lstm).

Distributed & Parallel Computing for Deep Learning Models

  • Parallel Training, Distributed vs Parallel Computing
  • Distributed computing in Tensorflow, Introduction to tf.distribute
  • Distributed training across multiple CPUs, Distributed Training
  • Distributed training across multiple GPUs, Federated Learning
  • Parallel computing in Tensorflow

Reinforcement Learning

  • Mapping the human mind with deep neural networks (dnns)
  • Several building blocks of artificial neural networks (anns)
  • The architecture of dnn and its building blocks
  • Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Deploying Deep Learning Models and Beyond

  • Understanding model Persistence, Saving and Serializing Models in Keras, Restoring and loading saved models
  • Introduction to Tensorflow Serving, Tensorflow Serving Rest, Deploying deep learning models with Docker & Kubernetes, Tensorflow Serving Docker, Tensorflow Deployment Flask.
  • Deploying deep learning models in Serverless Environments
  • Deploying Model to Sage Maker
  • Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow

Introduction to MLOps 

  • MLOps lifecycle
  • MLOps pipeline 
  • MLOps Components, Processes, etc

Deploying Machine Learning Models 

  • Introduction to Azure Machine Learning 
  • Deploying Machine Learning Models using Azure

Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI. 
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX 

  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics  

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity 
  • Embedded Power BI API and Power BI Mobile 
  • Power BI Advance and Power BI Premium

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

  • Extracting, loading and transforming data into usable format to gather insights. 
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements. 
  • Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
  • Assessment and monitoring of the model created using the machine learning models.

Introduction to Big Data And Spark

  • Apache spark framework, RDDs, Stopgaps in existing computing methodologies

RDDs 

  • RDD persistence, caching, General operations: Transformation, Actions, and Functions.
  • Concept of Key-Value pair in RDDs, Other pair, two pair RDDs
  • RDD Lineage, RDD Persistence, WordCount Program Using RDD Concepts
  • RDD Partitioning & How it Helps Achieve Parallelization

Advanced Concepts & Spark-Hive

  • Passing Functions to Spark, Spark SQL Architecture, SQLContext in Spark SQL
  • User-Defined Functions, Data Frames, Interoperating with RDDs
  • Loading Data through Different Sources, Performance Tuning
  • Spark-Hive Integration
  • Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
  • Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
  • Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
  • Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
  • Object Detection – A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
  • Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you some really helpful and actionable insights for specific stocks.
  • Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
  • AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.
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Program Highlights

50+ Live Session across 7 months
218 Hrs of Self-Paced Learning
30+ Industry Projects & Case Studies
24*7 Support

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.

Projects

Projects will be a part of your Certification in Data Science & Artificial Intelligence to consolidate your learning. It will ensure that you have real-world experience in Data Science and AI.

Practice 20+ Essential Tools

Designed by Industry Experts

Get Real-world Experience

Reviews

5

Hear From Our Hiring Partners

Career Services By Intellipaat

Career Services
Resume

Career Oriented Sessions

Throughout the course

Over 20+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.

Resume

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at profile shortlisting stage

Resume

Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

Resume

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, past experience, and future career aspirations.

Resume

3 Guaranteed Interviews

After 80% of the course completion

Guaranteed 3 job interviews upon submission of projects and assignments. Get interviewed by our 400+ hiring partners.

Resume

Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

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Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends – the possibilities are endless and our community has something for everyone!

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Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Submit Application

Submit Application

Tell us a bit about yourself and why you want to join this program

Application Review

Application Review

An admission panel will shortlist candidates based on their application

Admission

Application Review

Selected candidates will be notified within 1–2 weeks

Program Fee

Total Admission Fee

$ 1,799

Upcoming Application Deadline 26th June 2022

Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.

Program Cohorts

Next Cohorts

Date Time Batch Type
Program Induction 26th June 2022 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 26th June 2022 08:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

How will I receive my certificate?

Upon completion of the Data Science and Artificial Intelligence training course and execution of the various projects in this program, you will receive a joint Advanced Certification in Data Science and Machine Learning from Intellipaat and IIT Madras.

The Advanced Certification in Data Science and AI course is conducted by leading experts from IIT Madras and Intellipaat who will make you proficient in these fields through online video lectures and projects. They will help you gain in-depth knowledge in Artificial Intelligence and Data Science, apart from providing hands-on experience in these domains through real-time projects.

After completing the course and successfully executing the assignments and projects, you will gain an Advanced Certification in Data Science and Machine Learning from Intellipaat and IIT Madras which will be recognized by top organizations around the world. Also, our job assistance team will prepare you for your job interview by conducting several mock interviews, preparing your resume, and more.

If you fail to attend any of the live lectures, you will get a copy of the recorded session in the next 12 hours. Moreover, if you have any other queries, you can get in touch with our course advisors or post them on our community.

There will be a 2-day campus immersion module at IIT Madras during which learners will visit the campus. You will learn from the faculty as well as interact with your peers. However, this is subject to COVID-19 situation and guidelines provided by the Institute. The cost of travel and accommodation will be borne by the learners. However, the campus immersion module is optional.

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What is included in this course?

  • Non-biased career guidance
  • Counselling based on your skills and preference
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while you work

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