IITM Pravartak certified

Artificial Intelligence &

Machine learning course

5-Months Weekend Program with

100% Job Placement Assistance

Now learn in English, Tamil

About IIT Madras & Incubation Cell

The Indian Institute of Technology Madras or IIT Madras is

recognized globally and holds the laureate of being the

No.1 engineering university in India. With a faculty of

international fame, bolstered with a highly motivated and

profound student community, IIT Madras stands true as an

Institute of Eminence. It is a public technical and research

university established by the Government of India. IITM

Incubation Cell nurtures technology ventures through their

start-up phase by providing all the support

GUVI In A Glance

Grab Ur Vernacular Imprint-GUVI (an IIT-Madras

Incubated Company) is World’s First Vernacular Ed-Tech

Learning Platform. Introduced by Ex PayPal Employees,

GUVI empowers students to master programming skills

with the comfort of their native language. Its mission is to

impart technical skills to all through focussed pedagogical

tools.
About the Co-Founders

Co - Founder at GUVI

20+ years of Technical Expertise

& more

Tech Women Entrepreneur who was selected

Sridevi For Google Developers’ Launchpad Program

CEO & Founder at GUVI

20+ years of Technical Expertise

& more

Built 7 Products from Scratch Mentored 1000+

Arun Prakash
students Hosted 200+ sessions & 25+ webinars

Co - Founder at GUVI

17+ years of experience with IT industry

Technologist with 9+ years of Entrepreneurial

experience & Member of the Syllabus Sub-
Bala Murugan
Committee at Anna University

IITM Pravartak certified Artificial Intelligence and

Machine Learning course

GUVI's Zen Class offers the leading edge course: IIT-M

Pravartak Certified AI with ML Program that guides you to
become an Artificial Intelligence & Machine Learning
Expert in just 6 Months. The goal of this course is to help
Students/working professionals such as developers, data
scientists & engineers to gain knowledge of Machine
Learning models & AI techniques. Equip yourselves with
the job-ready skills required to implement core concepts
like Data transformation, AI knowledge & ML algorithms in
your work through our project-based & vernacular
upskilling approach.

 Why GUVI’s

Zen Class?

175% Highest Hike ₹21 Lakhs Highest Salary

40% Hike in Demand 200+ Hiring Partners

360+ Best Industry

100% Placement

Experts
Support
Pre-Program Phase

Candidate

Book a Seat with ₹8000

(100% Refundable)

Attend Pre-Program Session

Take Assessment

If Selected

Proceed to AI and Machine Learning

Program

₹89,999(Course Fee) - ₹8000(Booking

Fee) = ₹81,999(Remaining Fee)

If not selected/Interested

Immediate Refund of ₹8000

 IITM Pravartak certified
Artificial Intelligence and
Machine Learning course

Fast-paced 5-Months Weekend

Live Online Classes

Hands-on Industry Projects + Hackathons

Technical Mentorship by Industry

Experts + Periodic Meetups

Practise on Coding Practise Platforms

CodeKata | WebKata | IDE

Mock Interviews

Placement Interview Eligibility Test

Proceed to Placement Phase

Placement Phase
IITM Pravartak certified Artificial Intelligence
and Machine Learning course

Enters Placement Window

Exclusive Skill-based

Job Notifications

Company-specific Expectations Setting

(Hits Interviews Arranged)

Tech-Guidance +

10 Interviews Guaranteed
 Top skills you’ll learn!

Use Python & SQL to access and analyze data from

several different data resources.

Build predictive models using a variety of supervised &

unsupervised ML algorithms.

Learn how to create context based generative

responses with AI

Dive deep into Deep Learning using PyTorch.

Optimize, tune, and improve algorithms according to

specific metrics like accuracy and speed.

Understand the basics of image & gesture recognition

using variations of Recurrent neural network (RNN)

Convolution Neural Networks. (CNN).

Compare the performance of learned models using

suitable metrics.
 Technologies covered

Python SQL Data Visualization

MatPlotLib Plotly & Seaborn Deep Learning

Natural Language
PyTorch Processing Computer Vision

Artificial
Jupyter Notebook intelligence
 Program Curriculum
Module-0: AI for everyone
This will be a part of the preboot session, where
we will talk about basic AI and how it is being
used in industries. Moreover, we will shed some
light on the applications of Deep Learning.

What is AI?

The terminology of AI

The power of Machine Learning in the current era

The limitations of Machine Learning

A soft introduction to Deep Learning

Some cool applications of Deep Learning

Module-1: Introduction to Artificial

Intelligence and current trends
We will formally introduce AI and the current
industry practices. We will discuss how to build
and deploy state-of-the-art AI products.

Introduction to AI

Machine Learning basics

Workflow of a Machine Learning projects

Introduction to Deep Learning and difference

between ML and DL

Inducing AI using ML and DL

How to choose an AI project?

Module-2: Introduction to Python

We will go through the basics of python with all

essential beginner friendly concepts of python

programming like datatypes, loops, data

structures and functions, followed by

assessments and assignments

Python - Basic

Why python ?

Python IDE

Hello World Program

Variables & Names

String Basics

List

Tuple

Dictionaries

Conditional Statements

For and While Loop

Functions

Numbers and Math Functions

Common Errors in Python

Module-3: Introduction to Python

(Advanced)

Since we have essential basics of python

we will see some advanced concepts like

comprehensions, file handling, regular

expressions, object oriented programming,

pickling and many more essential concepts.

Python - Advanced

Functions as Arguments

List Comprehension

File Handling

Debugging in Python

Class and Objects

Lambda, Filters and Map

Python PIP

Iterators

Pickling

Python JSON

Python API and web scraping

 Module-4: Introduction to Pandas

for Data Handling

Since we need to handle huge amounts of

data, we will be implementing data handling

techniques with Pandas library.And we will

explore the different miscellaneous functions

of Pandas library in detail.

Introduction to Pandas

Series Data Structure - Querying and Indexing

DataFrame Data Structure - Querying, Indexing,

and loading

Merging data frames

Group by operation

Pivot table

Date/Time functionality

Example: Manipulating DataFrame

Module-5: Introduction to SQL

We will dive into the SQL-based databases. We

will understand the problems with file-based

systems and how databases can overcome

those challenges. We will learn the basics of SQL

queries, schemas, and normalization.

Data Modeling

Normalization, and Star Schema

ACID transactions

Select , insert , update & delete (DML and DQL)

Join operations

Window functions (rank, dense rank, row number

etc)

Data Types, Variables and Constants

Conditional Structures (IF, CASE, GOTO and NULL) 

Integrating python with SQL

 Module-6: Exploratory Data

Analysis with Python

It is always needed to analyze the data and

preprocess it , since the real world data is not

always industry ready, so in this week we will

be dealing with a lot of data cleaning and

Exploratory data Analysis techniques which is a

very crucial stage for any data science project

Structured vs Unstructured Data

Common Data issues and how to clean them

Textual data cleaning

Meaningful data transformation (Scaling and

Normalisation)

Handling missing data

Outlier detection and correction

Example: EDA on Movies DataSet

Module-7: Data Visualisation in

Python (Matplotlib, Seaborn)

Data Visualization is used to understand data in

visual context so that the patterns , trends and

correlations in the data can be understood. We

will do a lot of visualization with libraries like

Seaborn, Matplotlib etc inturn that leads to

effective storytelling.

Read Complex JSON files

Styling Tabulation

Distribution of Data - Histogram

Box Plot

Pie Chart

Donut Chart

Stacked Bar Plot

Relative Stacked Bar Plot

Stacked Area Plot

Scatter Plots

Bar Plot

Continuous vs Continuous Plot

Line Plot

Line Plot Covid Data

 Module-8: Machine Learning

Refresher

We will cover the basics of Machine Learning and

connect the use cases in the domain of Machine

Learning with Artificial Intelligence.

What is ML and how is it related to AI?

Predictive Modeling

Correlation

Basics of regression

Ordinary least squares

Simple linear regression

Model building

Model assessment and improvement

Diagnostics

Multiple linear regression (model building and

assessment)

Random forest & decision tree

Module-9: Machine Learning

Continued

We will cover more advanced concepts in ML.

Classification

Logistic regression

K nearest neighbours

Clustering

K means

Dimensionality reduction methods

Principal component analysis and its variants

Linear Discriminant Analysis

Support vector machine

Module-10: Introduction to

Neural Networks

Given the fundamental understanding of basic

regression algorithms, we will now deep dive into

the Neural Networks. We will learn the basic unit

of neural networks and will slowly learn to create

a network.

A single neuron details

The XOR problem and introduction to multi-layer

perceptron

Understanding the output & Activation Functions

Derivatives of Activation Functions

Gradient Descent for Neural Networks

Backpropagation Algorith m

Understanding Computational graph

Backpropagation using computational grap h

z
Random initiali ation
 Module-11: Deep Neural

Networks

After having the basic understanding of neural

networks, we will look into deep neural networks

and try to understand how to learn complex

functions.

Deep L-layer Neural Network

Forward Propagation in a Deep Network

Building Blocks of Deep Neural Networks

Forward and Backward Propagation

Parameters vs Hyperparameters

Parameters learning and hyperparameters tuning

Module-12: Applied Deep

Learning with Pytorch

We will dive into the practical aspects of deep

learning using PyTorch. We will learn the basic

terminologies and their significance. Moreover,

we will learn how to implement neural networks

in PyTorch.

Understanding the learning aspect of neural

networks

PyTorch basics

Tensor and Datasets in PyTorch

Linear Regression in PyTorch

Multiple Input Output Linear Regression

Softmax Regression

Shallow Neural Networks

Splitting the data (train/test/dev)

Understanding Bias and Variance

Understanding overfitting

Using regularization

Regularization techniques (like dropout)

 Module-13: Applied Deep Learning

with Pytorch Continued

We will learn normalization and other related

concepts. Moreover, we will look into the

problems like vanishing gradient

Implementing Deep Networks

Convolutional Neural Network (Convolution,

Activation Functions and Max Polling, Multiple

Input and Output Channels, GPU in PyTorch)

Normalizing Inputs

Vanishing / Exploding Gradients

Weight Initialization for Deep Networks

Numerical Approximation of Gradients

Gradient Checking

Gradient Checking Implementation

Module-14: Introduction to

Computer Vision with Convolution

Neural Networks

We will introduce computer vision and will try to

understand how deep learning can help us

perform various tasks.

What is a CV? (understanding with examples)

Edge detection with examples

Padding

Strided Convolutions

Convolutions Over Volume

One Layer of a Convolutional Network

Simple Convolutional Network Example

Pooling Layers

CNN Example
 Module-15: Natural Language

Processing with Neural Networks

Given the idea about Computer Vision with Deep

Neural Networks, now we will understand another

use case, which is NLP with deep learning.

Deep learning architectures for sequence

processing

Recurrent neural networks

Managing context in RNNs and its drawbacks

Introduction to LSTMs and GRUs

Module-16: Natural Language

Processing with Neural

Networks continued

After having the basic understanding of deep

learning architecture for language models, we will

now look into more complex architectures.

Self Attention Networks: Transformers

Introduction to Encoder-Decoder models

Encoder-Decoder with RNNs

Attention and Beam search

Encoder and Decoder with Transformers

Transfer Learning through Fine-Tuning

Module-17: Introduction to

LLMs and prompt Engineering

Given the overall context of transformer models

and transfer learning, now we will discuss the

architecture of Large Language Models and how

to write efficient prompts.

Introduction to Large Language Models

Description of GPT-3 and chatGPT architecture

Application of LLMs in various fields (Life sciences,

Legal Languages, etc.)

Basic description of other LLMs

 Module-18: Prompt

Engineering using OpenAI

We will now dive deeper into the prompt

engineering and discuss the effective ways of

using OpenAI API.

Introduction to GPT 3.5 & 4 api's

Introduction & importance of Prompt Engineering

Prompting Guidelines

Outcomes of Prompt Engineering - Iterative

learning, Summarizing, Inferring & Expanding

Interactive ChatBot

Application to summarize & identify the sentiment

of customer feedback given to an e-commerce

website

Final Projects

Image segmentation using DNNs

Gesture recognition using DNNs

Building NER for pharmaceutical dataset

Building and deploying Question Answering

system with Hugging Face

Face detection using Neural Style Transfer

Instructors
Learn from India’s top Industry Leaders

Dr Sanatan Sukhija Dr Yayati Gupta

Doctorate - IIT-Ropar Doctorate - IIT-Ropar

Mr Bala Chandar Mr Koushik Krishnan

Data Scientist,
Data Science Analyst at
US based client Credit Suisse

Mr Jeyaprakash Jayaraman Mr Vinish Vivek

Data Analyst
Consultant - Python
Target

Mr Shyam Kumar Dr Neeru Dubey

Machine Learning Research Scientist
Solutions Lead

Mr Abhishek Mr Thillaikkarasan M
Data Scientist,
Lead data scientist
BOSCH

Mr Jagadeesh Rajarajan Dr. Amit Kumar Verma

Senior staff engineer: Research Scientist & Sr.
data science Tech advisor, GUVI

Ms Anachal Varma
Senior Data Scientist

Freshworks
Hear it from our learners

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Sridharan K A nbazhagan
 Who can take this course?
Students pursuing degrees in computer science,
engineering, mathematics, statistics, or related
fields can upgrade their skill set with AI & ML

Working professionals from different industries

who wish to transition careers into AI and

ML-related roles

Anyone with a keen interest in upgrading their

future with AI & ML skills
Program Details

5-Months Weekend

Upto 12 Months
 Step into the

sought-after field of AI

&

Learn to build predictive

models from scratch!

IITM Research park - phase 2

module #9, 3rd floor, D block,

Kanagam Rd, Tharamani, Chennai,

Tamil Nadu, India. 600113