8 months

Live Training

18+

Guided Projects

240 Days

Cloud Lab Access

Placement

Assistance

3 Days

Campus Immersion

E&ICT, IIT Roorkee

Certificate

Course Overview

Home All Courses Course on Generative AI

Course on Generative AI

The PG Certificate Course in Data Science, AI/ML & Data Engineering is an intensive online instructor led course. You will master Machine Learning, Artificial Intelligence, ChatGPT Architecture, Data Engineering, Python, Spark, Hadoop, Classification, Regression, SVM, ANN, Tableau and more.

This Data Science course will empower you to solve complex problems and make impactful data-informed decisions. On completing the course successfully, you will receive a certificate from IIT Roorkee that can propel your career.

(4.75K) 35K+ Learners
18 Projects 240 Days Cloud Lab Access
Estimated 11.5M new Data Science jobs(US)
Avg. Salary of over $84000 in Data Science roles
High demands in Tech, Finance, E-Commerce, Healthcare
Highly transferable mainstream skills

Program Highlights

Key Highlights

8+ Months of Blended Training
240 Days of Lab access
18+ Projects
24*7 Support
Placement assistance
Lifetime Access to Course Material
Doubt clearing all weekdays
Scholarships available
3 Days Campus Immersion at IIT Roorkee
Certificate from E&ICT Academy-IIT Roorkee
15+ Languages and Tools covered
Hands-on experience in cloud labs

Book Counselling Session

Submit

Application Deadline 30 March 2023

Apply Now (3 Seats Left) Request a Callback

Our Students Work At

and more!

What is the certificate like?

Executive PG Certificate Program in Data Science By IIT Roorkee

  • About E&ICT Academy, IIT Roorkee

    E&ICT-IITR provides certification courses with emphasis on hands-on learning in basic/advanced topics and emerging technologies in Electronics and ICT. It is sponsored by the Ministry of Electronics and Information Technology, Govt. of India.

  • About Cloudxlab

    Cloudxlab is a team of developers, researchers, and educators who build innovative products and create enriching learning experiences for users. Cloudxlab upskills engineers in deep tech to make them employable & future-ready.

  • Get This Certificate

Campus Immersion Program

1:1 with Professors and Industry Experts
Certificate award ceremony
Network with your peers
Showcase your course project to Professors and peers
Once in a lifetime experience
QS

#1st

Among the IITs in the ‘Citations per Faculty’ parameter

*QS World Rankings

India Today

#5

Ranked Engineering College

*India Today 2020

NIRF

#6

Ranked for IITs

*NIRF 2020

QS

#12

Ranked Best Global Universities in India

*QS World Rankings

Hands-on Learning

hands-on lab
  • Gamified Learning Platform
    Making learning fun and sustainable

  • Auto-assessment Tests
    Learn by writing code and executing it on lab

  • No Installation Required
    Lab comes pre-installed softwares and accessible everywhere

  • Accessibility
    Access the lab anywhere, anytime with an internet connection

Mentors / Faculty

Instructor Tharun Reddy

Tharun Kumar Reddy Bollu

Faculty ECE Dept

IIT Roorkee

Instructor Raksha Sharma

Raksha Sharma

Faculty CSE Dept

IIT Roorkee

Instructor Sanjeev Manhas

Sanjeev Manhas

Faculty ECE Dept

IIT Roorkee

Dr. M.L. Virdi

Dr. M.L. Virdi

Senior Research Scientist

NASA

Instructor Sandeep Giri

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Instructor Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Instructor Praveen

Praveen Pavithran

Co-Founder at Yatis

Past: YourCabs, Cypress Semiconductor

Instructor Jatin Shah

Jatin Shah

Ex-LinkedIn, Yahoo, Yale CS Ph.D.

IIT-B

Curriculum

Foundation Courses

1. Programming Tools and Foundational Concepts
1. Linux for Data Science
2. Getting Started with Git
3. Python Foundations
4. Machine Learning Prerequisites(Including Numpy, Pandas and Linear Algebra)
5. Getting Started with SQL
6. Statistics Foundations

Course on Machine Learning

1. Machine Learning Applications & Landscape
1. Introduction to Machine Learning
2. Machine Learning Application
3. Introduction to AI
4. Different types of Machine Learning - Supervised, Unsupervised
2. Building end-to-end Machine Learning Project
1. Machine Learning Projects Checklist
2. Get the data
3. Launch, monitor, and maintain the system
4. Explore the data to gain insights
5. Prepare the data for Machine Learning algorithms
6. Explore many different models and short-list the best ones
7. Fine-tune model
3. Training Models
1. Linear Regression
2. Gradient Descent
3. Polynomial Regression
4. Learning Curves
5. Regularized Linear Models
6. Logistic Regression
4. Classification
1. Training a Binary classification
2. Multiclass,Multilabel and Multioutput Classification
3. Performance Measures
4. Confusion Matrix
5. Precision and Recall
6. Precision/Recall Tradeoff
7. The ROC Curve
5. Support Vector Machines
1. Linear SVM Classification
2. Nonlinear SVM Classification
3. SVM Regression
6. Decision Trees
1. Training and Visualizing a Decision Tree
2. Making Predictions
3. Estimating Class Probabilities
4. The CART Training Algorithm
5. Gini Impurity or Entropy
6. Regularization Hyperparameters
7. Instability
7. Ensemble Learning and Random Forests
1. Voting Classifiers
2. Bagging and Pasting
3. Random Patches and Random Subspaces
4. Random Forests
5. Boosting and Stacking
8. Dimensionality Reduction
1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction
3. PCA
4. Kernel PCA
5. LLE
6. Other Dimensionality Reduction Techniques

Course on Deep Learning and Reinforcement Learning

1. Introduction to Artificial Neural Networks
1. From Biological to Artificial Neurons
2. Implementing MLPs using Keras with TensorFlow Backend
3. Fine-Tuning Neural Network Hyperparameters
2. Convolutional Neural Networks and Computer Vision
1. The Architecture of the Visual Cortex
2. Convolutional Layer
3. Pooling Layer
4. CNN Architectures
5. Classification with Keras
6. Transfer Learning with Keras
7. Object Detection
8. YOLO
3. Recurrent Neural Networks
1. Recurrent Neurons and Layers
2. Basic RNNs in TensorFlow
3. Training RNNs
4. Deep RNNs
5. Forecasting a Time Series
6. LSTM Cell
7. GRU Cell
4. Natural Language Processing
1. Introduction to Natural Language Processing
2. Creating a Quiz Using TextBlob
3. Finding Related Posts with scikit-learn
4. Generating Shakespearean Text Using Character RNN
5. Sentiment Analysis
6. Encoder-Decoder Network for Neural Machine Translation
7. Attention Mechanisms
8. Recent Innovations in Language Models
5. Training Deep Neural Networks
1. The Vanishing / Exploding Gradients Problems
2. Reusing Pretrained Layers
3. Faster Optimizers
4. Avoiding Overfitting Through Regularization
5. Practical Guidelines to Train Deep Neural Networks
6. Custom Models and Training with Tensorflow
1. A Quick Tour of TensorFlow
2. Customizing Models and Training Algorithms
3. Tensorflow Functions and Graphs
7. Loading and Preprocessing Data with TensorFlow
1. Introduction to the Data API
2. TFRecord Format
3. Preprocessing the Input Features
4. TF Transform
5. The TensorFlow Datasets (TDFS) Projects
8. Autoencoders and GANs
1. Efficient Data Representations
2. Performing PCA with an Under Complete Linear Autoencoder
3. Stacked Autoencoders
4. Unsupervised Pre Training Using Stacked Autoencoders
5. Denoising Autoencoders
6. Sparse Autoencoders
7. Variational Autoencoders
8. Generative Adversarial Networks
9. Reinforcement Learning
1. Learning to Optimize Rewards
2. Policy Search
3. Introduction to OpenAI Gym
4. Neural Network Policies
5. Evaluating Actions: The Credit Assignment Problem
6. Policy Gradients
7. Markov Decision Processes
8. Temporal Difference Learning and Q-Learning
9. Deep Q-Learning Variants
10. The TF-Agents Library

Recent Innovations in AI

1. OpenAI's ChatGPT
1. Introduction to ChatGPT
2. Architecture of GPT
3. ChatGPT Architecture and Training
2. Stable Diffusion
1. Introduction to Stable Diffusion
2. Stable Diffusion Components
3. Diffusion Model
4. Stable Diffusion Architecture and Training

Course on Data Engineering

1. Introduction to Hadoop
1. Introduction
2. Distributed systems
3. Big Data Use Cases
4. Various Solutions
5. Overview of Hadoop Ecosystem
6. Spark Ecosystem Walkthrough
2. Foundation & Environment
1. Understanding the CloudxLab
2. Getting Started - Hands on
3. Hadoop & Spark Hands-on
4. Understanding Regular Expressions
5. Setting up VM
3. Zookeeper
1. ZooKeeper - Race Condition
2. ZooKeeper - Deadlock
3. How does election happen - Paxos Algorithm?
4. Use cases
5. When not to use
4. HDFS
1. Why HDFS?
2. NameNode & DataNodes
3. Advance HDFS Concepts (HA, Federation)
4. Hands-on with HDFS (Upload, Download, SetRep)
5. Data Locality (Rack Awareness)
5. YARN
1. Why YARN?
2. Evolution from MapReduce 1.0
3. Resource Management: YARN Architecture
4. Advance Concepts - Speculative Execution
6. MapReduce Basics
1. Understanding Sorting
2. MapReduce - Overview
3. Word Frequency Problem - Without MR
4. Only Mapper - Image Resizing
5. Temperature Problem
6. Multiple Reducer
7. Java MapReduce
7. MapReduce Advanced
1. Writing MapReduce Code Using Java
2. Apache Ant
3. Concept - Associative & Commutative
4. Combiner
5. Hadoop Streaming
6. Adv. Problem Solving - Anagrams
7. Adv. Problem Solving - Same DNA
8. Adv. Problem Solving - Similar DNA
9. Joins - Voting
10. Limitations of MapReduce
8. Analyzing Data with Pig
1. Pig - Introduction
2. Pig - Modes
3. Example - NYSE Stock Exchange
4. Concept - Lazy Evaluation
9. Processing Data with Hive
1. Hive - Introduction
2. Hive - Data Types
3. Loading Data in Hive (Tables)
4. Movielens Data Processing
5. Connecting Tableau and HiveServer 2
6. Connecting Microsoft Excel and HiveServer 2
7. Project: Sentiment Analyses of Twitter Data
8. Advanced - Partition Tables
9. Understanding HCatalog & Impal
10. NoSQL and HBase
1. NoSQL - Scaling Out / Up
2. ACID Properties and RDBMS Story
3. CAP Theorem
4. HBase Architecture - Region Servers etc
5. Hbase Data Model - Column Family Orientedness
6. Getting Started - Create table, Adding Data
7. Adv Example - Google Links Storage
8. Concept - Bloom Filter
9. Comparison of NOSQL Databases
11. Importing Data with Sqoop and Flume, Oozie
1. Sqoop - Introduction
2. Sqoop Import - MySQL to HDFS
3. Exporting to MySQL from HDFS
4. Concept - Unbounding Dataset Processing or Stream Processing
5. Flume Overview: Agents - Source, Sink, Channel
6. Data from Local network service into HDFS
7. Example - Extracting Twitter Data
8. Example - Creating workflow with Oozier
12. Introduction to Spark
1. Apache Spark ecosystem walkthrough
2. Spark Introduction - Why Spark?
13. Scala Basics
1. Introduction, Access Scala on CloudxLab
2. Variables and Methods
3. Interactive, Compilation, SBT
4. Types, Variables & Values
5. Functions
6. Collections
7. Classes
8. Parameters
14. Spark Basics
1. Apache Spark ecosystem
2. Why Spark?
3. Using the Spark Shell on CloudxLab
4. Example 1 - Performing Word Count
5. Understanding Spark Cluster Modes on YARN
6. RDDs (Resilient Distributed Datasets)
7. General RDD Operations: Transformations & Actions
8. RDD lineage
9. RDD Persistence Overview
10. Distributed Persistence
15. Writing and Deploying Spark Applications
1. Creating the SparkContext
2. Building a Spark Application (Scala, Java, Python)
3. The Spark Application Web UI
4. Configuring Spark Properties
5. Running Spark on Cluster
6. RDD Partitions
7. Executing Parallel Operations
8. Stages and Tasks
16. Common Patterns in Spark Data Processing
1. Common Spark Use Cases
1. Example 1 - Data Cleaning (Movielens)
1. Example 2 - Understanding Spark Streaming
2. Understanding Kafka
3. Example 3 - Spark Streaming from Kafka
4. Iterative Algorithms in Spark
5. Project: Real-time analytics of orders in an e-commerce company
17. Data Formats & Management
1. XML
2. AVRO
3. How to store many small files - SequenceFile?
4. Parquet
5. Protocol Buffers
6. Comparing Compressions
7. Understanding Row Oriented and Column Oriented Formats - RCFile?
18. DataFrames and Spark SQL
1. Spark SQL - Introduction
2. Spark SQL - Dataframe Introduction
3. Transforming and Querying DataFrames
4. Saving DataFrames
5. DataFrames and RDDs
6. Comparing Spark SQL, Impala, and Hive-on-Spark
19. Machine Learning with Spark
1. Machine Learning Introduction
2. Applications Of Machine Learning
3. MlLib Example: k-means
4. SparkR Example
8+
Months of Blended Training
240
Days of Lab Access
18+
Projects
13K+
Learners
Download Curriculum

Projects

Starting at $ 84/month

Program Fee: $ 2,499
(earn scholarship upto 750)

Take the Test Now

Low cost EMI

Recommended

Starting at $ 84/month

VIEW ALL EMI PLANS | Scholarship available

Apply Now

Admission Process


  • Step 1. Take the Scholarship Test

  • Step 2. Test results will be announced in 24 hours

  • Step 3. Join the Prestigious Program
    The admission office will send the letter of acceptance. Submit the admission fees in due time to confirm the seat

    • Note: Admission test should be taken immediately after submitting the application using the link displayed post application submission.


Eligibility Criteria

    1. Anybody in their final year of undergraduate degree or has completed their undergraduation is eligible to apply for the course
    1. Must have studied Mathematics in 12th standard

Additional Scholarships

    1. 5% Scholarships are available for students, women from STEM background and unemployed
    1. 5% Scholarship available for IIT Alumni and CloudxLab Alumni.
    1. Bring your friend along and avail discount upto 5%.

PS: Details to avail the scholarship will be sent post application-submission and only one scholarship applicable per learner

Certification Guideline

Learners are expected to complete at least 80% of the course content and any 3 of the mandatory projects within 240 days of batch commencement to be eligible for the certificate.

Earn Scholarship of Rs. 50,000 in Just One Hour!

    1. Get Rs. 1,000 OFF for every correct answer.
    1. Test includes 50 questions (Duration: 1 hour).
    1. You can take the test anytime, anywhere!
    1. Best of first three attempts will be considered.

Scholarship Test

Take the Test Now

Placement Assistance

By CloudxLab

Placement Eligibility Test

Placement Eligibility Test

We have around 300+ recruitment partners who will be interviewing you based on your performances in PET

Dedicated Job Portal

Dedicated Job Portal

Opportunities from companies who approach us asking for our learner profiles will be posted on our job portal to providevisibility to your profile

Career Guidance Webinars

Career Guidance Webinars

Career Guidance Webinars from seasoned industry experts

Testimonials

​