12 months

Live Training

36+

Real-world Projects

365 Days

Cloud Lab Access

Placement

Assistance

3 Days

Campus Immersion

CEC, IIT Roorkee

Certificate

Course Overview

Home All Courses PG Certificate Program in Data Science & AI By CEC, IIT Roorkee

PG Certificate Program in Data Science & AI By CEC, IIT Roorkee

The PG Certificate Course in AI, Machine Learning, Generative AI & Data Science is an intensive online instructor led course tailored to the evolving ML landscape. You will master Machine Learning, Deep Learning, Artificial Intelligence, ChatGPT Architecture, Data Engineering, Python, Spark, Hadoop, Stable Diffusion, Transformers, Tableau and more.

The landscape of the ML industry has transformed since GPT-3's introduction. In response, we've updated our curriculum to embrace these shifts. Our course delves into recent innovations like Langchain, Vector DBs, Prompt Engineering and developing LLM-powered apps. On completing the course successfully, you will receive a certificate from IIT Roorkee that will propel your career.

(4.75K) 35K+ Learners
36+ Real-world Projects 365 Days Cloud Lab Access
Campus Immersion at IITR
Estimated 11.5M new Data Science jobs(US)
Avg. CTC ranges from $15000 to 45000 in Data Science roles
High demands in Tech, Finance, E-Commerce, Healthcare
Highly transferable mainstream skills

Program Highlights

Key Highlights

12+ Months of Blended Training
365 Days of Lab access
36+ Real-world 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 CEC-IIT Roorkee
Latest AI Trends Incorporated in Curriculum
30+ Tools and Technologies covered
Hands-on experience in cloud labs
Interactive Learning for Rapid Mastery

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Application Deadline 28 April 2024

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Certificate

What is the certificate like?

  • About IIT Roorkee

    IITR provides certification courses with emphasis on hands-on learning in basic/advanced topics and emerging technologies. It was formed in 1955 and upskills learners in the latest development in Engg. and Science and Technology.

  • 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

QS

#12

Ranked Best Global Universities in India

*QS World Rankings

NIRF

#8

Ranked for IITs

*NIRF 2023

India Today

#5

Ranked Engineering College

*India Today 2020

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 Raksha Sharma

Dr. Raksha Sharma

Faculty CSE Dept

IIT Roorkee

Coordinator Kaushik Ghosh

Kaushik Ghosh

Coordinator

IIT Roorkee

Instructor Sandeep Giri

Sandeep Giri

Founder at CloudxLab

Past: Amazon, InMobi, D.E.Shaw

Dr. M.L. Virdi

Dr. M.L. Virdi

Senior Research Scientist

NASA

Instructor Abhinav Singh

Abhinav Singh

Co-Founder at CloudxLab

Past: Byjus

Instructor Praveen

Praveen Pavithran

Co-Founder at Yatis

Past: YourCabs, Cypress Semiconductor

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. Types of Machine Learning algorithms - Supervised, Unsupervised
2. Building end-to-end Machine Learning Project
1. Machine Learning Projects Checklist
2. Get the data
3. Explore the data to gain insights
4. Prepare the data for Machine Learning algorithms
5. Explore many different models and short-list the best ones
6. Fine-tune model
7. Launch, monitor, and maintain the system
3. Training Models
1. Linear Regression
2. Gradient Descent
3. Model Evaluation and Metrics
4. Polynomial Regression
5. Overfitting and Underfitting
6. Regularized Linear Models
7. Logistic Regression
4. Classification
1. Training a Binary classifier
2. Multiclass, Multilabel and Multioutput Classification
3. Performance Metrics
4. Confusion Matrix
5. Precision and Recall
6. Precision/Recall Tradeoff
7. The ROC Curve
5. Support Vector Machines
1. Introduction to Support Vector Machines
2. SVM for Classification
3. SVM for Regression
4. HyperParameter Tuning
6. Decision Trees
1. Training and Visualizing a Decision Tree
2. Making Predictions
3. The CART Training Algorithm
4. HyperParameter Tuning
5. Handling overfitting
7. Ensemble Learning
1. Introduction to Ensemble Learning
2. Demonstrating Why Multiple Models Attain Superior Accuracy
3. Types of Ensemble Learning methods
8. Dimensionality Reduction
1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction
3. Principal Component Analysis (PCA)

Course on Deep Learning and Reinforcement Learning

1. Introduction to Artificial Neural Networks
1. From Biological to Artificial Neurons
2. Backpropogation from Scratch
3. Activation Functions
4. Implementing MLPs using Keras with TensorFlow Backend
5. 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. Stable Diffusion
1. Introduction to Stable Diffusion
2. Stable Diffusion Components
3. Diffusion Model
4. Stable Diffusion Architecture and Training
4. 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
5. Natural Language Processing
1. Introduction to Natural Language Processing
2. Word Embeddings
3. Creating a Quiz Using TextBlob
4. Finding Related Posts with scikit-learn
5. Sentiment Analysis
6. Encoder-Decoder Network for Neural Machine Translation
6. 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
7. Custom Models and Training with Tensorflow
1. A Quick Tour of TensorFlow
2. Customizing Models and Training Algorithms
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

Course on Large Language Models

1. Transformers
1. Attention Mechanism
2. Transformer Architecture and components
3. Transfer Learning
4. Transformer Variants
2. OpenAI's ChatGPT
1. Introduction to ChatGPT
2. Architecture of GPT
3. ChatGPT Architecture and Training
3. Vector Databases
1. Introduction to Vector Databases
2. Architecture of Vector Databases
3. Indexing Techniques
4. Distance Metrics and Similarity Measures
5. Nearest Neighbor Search
6. Open Source Vector Databases:- Chroma and Milvus
4. Langchain
1. Introduction to Langchain
2. The building blocks of LangChain:- Prompt, Chains, Retrievers, Parsers, Memory and Agents
5. Creating LLM powered apps with Langchain
1. Demonstration:- Building personalized chatbot using Langchain

Course on Large-Scale System Design (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
12+
Months of Blended Training
365
Days of Lab Access
36+
Projects
35K+
Learners
Download Curriculum

Projects

EMI Starting at  155/month

Course Fee: 3,499 4,999
(Additional Scholarship Available 500)

Batch 9

(28 January 2024)

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Batch 10

(28 April 2024)

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Low cost EMI

Recommended

Starting at $ 155/month

VIEW ALL EMI PLANS | Scholarship available

Earn Scholarship of 500 in Just 30 Minutes!

    1. Get 20 OFF for every correct answer.
    1. Test includes 25 questions (Max Duration: 30 Mins).
    1. Get straight 500 OFF on answering all questions correctly.
    1. You can take the test anytime, anywhere!
    1. Best of first three attempts will be considered.

Scholarship Test

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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 365 days of batch commencement to be eligible for the certificate.

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

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Frequently Asked Questions

Will I get support?

Yes! Please feel free to ask your questions on CloudxLab forum and our community and team of experts will answer your questions. We believe forum will add better perspectives, ideas, and solutions to your questions.

Can I get a certificate for the projects completed?

We have created a set of Guided Projects on our platform. You may complete these guided projects and earn the certificate for free. Check it out here

Can I expect any placement support?

Yes, we do offer placement assistance that includes career guidance, resume building tips and mock interviews. Each participant will receive staunch support from the industry mentors, who also direct you through various placement opportunities within the industry. Above all, we are partnered with leading MNC’s that offer placement opportunities to our participants.

Do I need to install any software before starting this course?

No, we will provide you with the access to our online lab and BootML so that you do not have to install anything on your local machine

What is the validity of course material?

We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access to the course material so that you can refer to the course material anytime.

What if I miss a class?

You will never lose any lecture. You can view the recorded session of the class in your LMS.

What is the refund policy?

If you are unhappy with the product for any reason, you can ask for a full refund up to 14 days after the start of the first live sessions. Please contact us at reachus@cloudxlab.com to request a refund within the stipulated time. We will be sorry to see you go though!

I have some more questions. Can I talk to someone?

Absolutely! Please contact us here. You can also reach us anytime on our 24/7 support helpline by calling us on +918049202224

Will there be Options to Pay using EMI/Installments

Yes, you can choose to pay by installments on the payment page.

How will the payment for the course be made?

The course fee will be paid in two parts:

Admission fee - This is a small percentage of the total course fee which helps to reserve your seat for the program

Installments - The remaining course fee will be paid in equal installments monthly as per the EMI option selected while enrolling in the course.

If I have more questions during the week days apart from the live sessions, how can I get it cleared?

Teaching assistants will be helping you during the weekdays to ensure a seamless learning experience. You will be able to have a session at your and the teaching assistants convenience and get your queries cleared over WhatsApp, email or call. The learners will be part of a WhatsApp group with the course faculty so that all the queries the learners may have are answered 24/7.

Is there an admission fee for the PG Certification Programs?

No, there are no additional charges for admission. But, if you are choosing to pay by EMI, there will be a fixed admission charge that is to be paid. This amount will be subtracted from the total course fees.