P O S T G R A D U AT E P R O G R A M I N

ARTIFICIAL
INTELLIGENCE
& MACHINE
LEARNING

YEARS OF
140+ SUCCESSFUL
5 EXCELLENCE BATCHES
 ABOUT THE PROGRAM

A relentless industry focus - that’s how With inputs from industry professionals,
the PGP-AIML has been able to top academicians, and recently
empower thousands of career graduated alums, the PGP-AIML is your
transitions. All parts of the program best bet for a rewarding Artificial
experience are designed to make Intelligence career.
learners job-ready. But here’s the
challenge - the industry keeps evolving
all the time. Only high-quality learning
has the power to transform lives, so we
have high standards for our programs.

6.6 Mn+ 170+ 5600+ 2400+

Learners Countries Industry Experts Companies Hire From Us

Best Ed-tech Best Ed-tech

Company Company
of the Year* Best Online Skills of the Year*
Indian Education Provider of theYear* EdTech Review
Awards 2022 Awards 2020
Entrepreneur, Education
Innovation Awards 2022 01
WHAT MAKES THE
PGP-AIML UNIQUE?
Covers Artificial Intelligence & Machine assignments you have done as part of this
Learning technologies and applications program in a single place. Today, most
including Machine Learning, Deep companies look at potential recruits'
Learning, Computer Vision, Natural Github profiles to check their technical
Language Processing, Reinforcement expertise before hiring them.
Learning, Neural Network, TensorFlow and
Designed by leading academic and
many more.
industry experts along with IIT-Bombay
12+ hands-on projects using AI and ML lab. faculty
This also features case studies, industry
sessions with leading experts and learning
from some of the top global companies
like Uber, Netflix, Google, Amazon etc. .

For every assignment you work as part of

this program, you will get to see the
solutions of the assignment as recorded
walkthroughs. Recorded walkthroughs
help you to understand the concepts
better and analyze a problem from the
view of an expert.

As part of this program, you will be

making all of your submissions on Github.
Github is an online repository which helps
you to store all the projects and

5 YEARS OF DELIVERING EXCELLENCE

34 LPA
Highest Salary

3.5
out of 4 Overall Program Rating

99%
Sessions Rated 4 and above out of 5

4.8
out of 5 Average Session Rating

02
PROGRAM PEDAGOGY

1. Program Delivery 4. Industry Perspective Lectures

The program is delivered in an online format This is an important component of the

with weekend mentorship sessions that span program that complements and substantiates
over 12 months. learning with an applied orientation. The
participants get the opportunity to listen to
2. Online-Learning Management System
eminent speakers from leading Artificial
All candidates have access to the online LMS Intelligence and Machine Learning companies
that hosts content (lecture recordings, and assimilate the best practices discussed by
discussion forums, assignments, reading them in their lectures.
material) and live webinars to enable the
5. Experiential Learning
candidates to continue their learning during
campus. The LMS provides an innovative This program is designed to transform
learning environment that encourages candidates to business-ready Artificial
collaborative approach between the Intelligence and Machine Learning
candidates thus paving the way for maximizing professionals through hands-on experiential
learning effectiveness. learning of relevant tools. This is achieved
through hands-on labs, practice exercises,
3. Capstone Project
hackathons, quizzes and assignments on
All candidates would be pursuing an software packages such as Python, NLP and
application-oriented capstone project in the Deep Learning.
field of Artificial Intelligence and Machine
Learning. The project shall be mentored and
evaluated by faculty from Great Lakes Institute
of Management or industry experts. The
project will be presented to the faculty board
as part of the requirement for successful
completion of the program.

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PROGRAM CURRICULUM

PYTHOM BOOTCAMP FOR NON-PROGRAMMERS

This bootcamp serves as a training module for learners with limited or no programming exposure.
It enables them to be at par with those learners who have prior programming knowledge, before
the program commences. This is an optional but open-to-all module. More than 1000 learners have
successfully leveraged it to create a strong foundation of programming knowledge necessary
to succeed as an AI/ML professional.

FOUNDATIONS MACHINE LEARNING

MODULE 1 MODULE 1
Introduction to Python Supervised learning
• Python Basics • Linear Regression
• Python Functions and Packages • Multiple Variable Linear Regression
• Working with Data Structures, Arrays, • Logistic Regression
Vectors & Data Frames • Naive Bayes Classifiers
• Jupyter Notebook – Installation & Function
• k-NN Classification
• Pandas, NumPy, Matplotlib, Seaborn
• Support Vector Machines
SELF PACED MODULE
EDA and Data Processing MODULE 2

• Data Types Ensemble Techniques

• Dispersion & Skewness • Decision Trees
• Uni & Multivariate Analysis • Bagging
• Data Imputation • Random Forests
• Identifying and Normalizing Outliers • Boosting

MODULE 2 MODULE 3
Applied Statistics
Unsupervised learning
• Descriptive Statistics
• K-means Clustering
• Probability & Conditional Probability
• Hierarchical Clustering
• Hypothesis Testing
• Dimension Reduction-PCA
• Inferential Statistics
• Probability Distributions MODULE 4
Featurisation, Model Selection & Tuning
• Feature Engineering
• Model Selection and Tuning

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• Model Performance Measures MODULE 3
• Regularising Linear Models NLP (Natural Language Processing)
• Ml Pipeline • Introduction to NLP
• Bootstrap Sampling • Stop Words
• Grid Search Cv
• Tokenization
• Randomized Search Cv
• Stemming and Lemmatization
• K Fold Cross-validation
• Bag of Words Model
MODULE 5
• Word Vectorizer
Recommendation Systems
• Introduction to Recommendation Systems • TF-IDF
• Popularity Based Model • POS Tagging
• Content based Recommendation System • Named Entity Recognition
• Collaborative Filtering (User similarity & Item similarity) • Introduction to Sequential data
• Hybrid Models • RNNs and its Mechanisms
• Vanishing & Exploding gradients in RNNs
ARTIFICIAL INTELLIGENCE • LSTMs - Long short-term memory
• GRUs - Gated Recurrent Unit
MODULE 1
• LSTMs Applications
Introduction to Neural Networks and Deep Learning
• Time Series Analysis
• Introduction to Perceptron & Neural Networks
• LSTMs with Attention Mechanism
• Activation and Loss functions
• Neural Machine Translation
• Gradient Descent
• Advanced Language Models:
• Batch Normalization
Transformers, BERT, XLNet
• TensorFlow & Keras for Neural Networks
• Hyper Parameter Tuning SELF PACED MODULE
Introduction to Reinforcement
MODULE 2 Learning (RL)
Computer Vision • RL Framework
• Introduction to Convolutional • Component of RL Framework
Neural Networks • Examples of RL Systems
• Introduction to Images • Types of RL Systems
• Convolution, Pooling, Padding & • Q-learning
its Mechanisms
SELF PACED MODULE
• Forward Propagation & Backpropagation
Introduction to GANs (Generative
for CNNs
Adversarial Networks)
• CNN architectures like AlexNet, VGGNet,
• Introduction to GANs
InceptionNet & ResNet • Generative Networks
• Transfer Learning • Adversarial Networks
• Object Detection • How do GANs work?
• YOLO, R-CNN, SSD • DCGANs - Deep Convolution GANs
• Semantic Segmentation • Applications of GANs
• U-Net
ADDITIONAL MODULE
• Face Recognition using Siamese Networks
• Power BI
• Instance Segmentation
• Cloud Computing
• Block Chain

05
TOOLS AND MORE

Python Scipy Matplotlib OpenCV

Numpy

statsmodels
TensorFlow Tkinter Pandas Flask Keras

PROJECTS
1. To identify the potential customers who have 3. To create an automation using computer
a higher probability to churn using ensemble vision to impute dynamic bounding boxes to
prediction model. locate cars or vehicles on the road.

A telecom company wants to use their historical City X’s traffic department wants to understand
customer data to predict behaviour to retain the traffic density on road during busy hours in
customers. You can analyse all relevant order to efficiently program their traffic lights.
customer data and develop focused customer
retention programs.
4. Implementing an Image classification
neural network to classify Street House View
2. To cluster the vehicles as per their fuel Numbers.
consumption attributes and later train a
Recognizing multi-digit numbers in
regression model for an automobile dataset.
photographs captured at street level is an
The purpose is to classify a given vehicle as important component of modern-day map
one of three types of vehicles, using a set of making. A classic example of a corpus of
features extracted from the silhouette. The such street-level photographs is Google’s
vehicle may be viewed from one of many Street View imagery composed of hundreds
different angles. of millions of geo-located 360-degree
panoramic images.

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 5. Predicting the condition of the patient
depending on the received test results.
This project has two parts. In the first
part we are trying to predict the
condition of the patient depending on
the received test results on
biomechanics features of the patients
according to their current conditions. In
part II, we need to design a supervised
learning prediction model to perform
targeted marketing for executing a
digital marketing campaign for a bank.

6. To build a NLP classifier which can

use input text parameters to
determine the label/s of the blog.
Classification is probably the most
popular task that you would deal
with in real life. Text in the form of
blogs, posts, articles, etc. is written
every second. It is a challenge to
predict the information about the
writer without knowing about
him/her. We are going to create a
classifier that predicts multiple
features of the author of a given text.
We have designed it as a Multi label
classification problem.

7. To build a recommendation system

The ability to automatically transcribe an address using popularity based and collaborative
number from a geolocated patch of pixels and filtering methods to recommend mobile
associate the transcribed number with a known phones to a user which are most popular
street address helps pinpoint, with a high degree and personalised respectively.
of accuracy, the location of the building it
India is the second largest market
represents. globally for smartphones after China.
More broadly, recognizing numbers in photographs About 134 million smartphones were
is a problem of interest to the optical character sold across India in the year 2017 and is
recognition community. While OCR on constrained estimated to increase to about 442
domains like document processing is well studied, million in 2022. India ranked second in
the average time spent on mobile web
arbitrary multi-character text recognition in
by smartphone users across Asia Pacific.
photographs is still highly challenging. This
difficulty arises due to the wide variability in the If a seller succeeds to publish
visual appearance of text in the wild on account of smartphones based on user’s
a large range of fonts, colors, styles, orientations, behaviour/choice at the right place,
there are 90% chances that user will
and character arrangements. The recognition
enquire for the same. This Case Study is
problem is further complicated by environmental
targeted to build a recommendation
factors such as lighting, shadows, secularities, and
system based on the individual
occlusions as well as by image acquisition factors
consumer’s behaviour or choice.
such as resolution, motion, and focus blur.
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8. Sarcasm Detection using towards employing autonomous AI bots. Computer
Bidirectional LSTMs vision can be used to automate supervision and
generate appropriate action triggers if the event is
Past studies in Sarcasm Detection mostly
predicted from the image of interest. For example a
make use of Twitter datasets collected
car moving on the road can be easily identified by a
using hashtag based supervision but such camera as make of the car, type, colour, number
datasets are noisy in terms of labels and plates, etc.
language. Furthermore, many tweets are
replies to other tweets and detecting 11. To build an image classifier and object detection
sarcasm in these requires the availability of model which can classify an chest X-ray image into
contextual tweets. In this hands-on project, with/without pneumonia disease and identify the
the goal is to build a model to detect location of the chest X-ray where the disease is
whether a sentence is sarcastic or not, localised by publishing a bounding box around it.
using Bidirectional LSTMs. The designed model can be used as a micro service
within an application which can assist hospitals and
9. To build a semi-rule based text chat bot patients to detect the health condition of the patient
which can give static responses to the user just by scanning the X-ray file generated. Project
depending on the inputs for industrial involves designing a full stack deep learning AI
safety and incidents solution covering data warehousing, data cleansing,
validation, machine learning model design/
Linguistic chatbots have become a must to
validation/tuning, model freezing/baseline and model
have automation for large organisations
deployment using a clickable UI.
with a huge client base. They serve as a
virtual support, Helpdesk, sales agents etc
12. To build an image classifier which can classify
enhancing the business and the customer
images of dogs as per their breeds.
experience.
Image classifiers have become a must to have
10. To build an image classifier and object automation for organisations chasing towards
detection model which can classify a car employing autonomous AI bots. This image classifier
from an image and identify the location of can be a micro service for identifying if the captured
the car from an image by publishing a image is a dog and later identifying which breed or
bounding box around it. class the animal belongs to. The learnings and
experience obtained to design this automation can
Image classifiers have become a must to
be trained and deployed on different image/class
have automation for organisations chasing dataset too.

08
FACULTY

DR. KUMAR MUTHURAMAN

H. Timothy (Tim) Harkins TESTIMONIALS
Centennial Professor
(University Of Texas at Austin)

DR. D NARAYANA
Faculty, AI and
MANISH KUMAR
Machine Learning Senior Engineer, Tata Consulting
(Great Learning) Engineering Limited
“The program learning experience has been
smooth and great. The program is well
structured and the learning content provided
is up-to-date and covers both theoretical and
industrial application aspects. Hands-on
PROF. MUKESH RAO
exercises and projects at the end of the
Faculty, Machine Learning module are really helpful in gaining
(Great Learning) confidence.”

PROF. ABHINANDA SARKAR

Academic Director
(Great Learning)

DHINESH KUMAR GANESHAN

Lead Consultant - Infosys
“Great Learning's PGP-AIML Course is an
eye-opener on future technologies and
opportunities and is led by industry experts
who put their efforts into ensuring that the
knowledge is shared in the right sense. They
try to help students to gain critical
information that is important for their
career success.”

Faculty has contributed to program curriculum and online learning content only.

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CAREER SUPPORT

E-PORTFOLIO JOB BOARD

An e-portfolio is a snapshot of all the projects The program provides candidates access to a
done and skills acquired during the program Job Board with job opportunities shared by
that is shareable across social media channels. 2400+ organisations. Gain an average salary
This will help you establish your expertise to hike of 50% like many other learners.
potential recruiters.

RESUME REVIEW AND INTERVIEW

PREPARATION

Build your resume to highlight your skills and

your previous professional experience. Also,
learn how to crack interviews with interview
preparation sessions.

CAREER GUIDANCE

Get access to career mentoring from industry

experts who’ve transitioned to Artificial
Intelligence and Machine Learning roles.
Benefit from their guidance on how to build a
rewarding career in Artificial Intelligence and
Machine Learning.

10
COMPANIES THAT HIRE
FROM US

+2400 MORE

11
ADMISSION DETAILS

Eligibility
Applicants should have a Bachelor's degree with a minimum of 50% aggregate marks or
equivalent and familiarity with programming. For candidates who do not know Python,
we offer a free pre-program tutorial.

Selection Process

APPLY INTERVIEW PROCESS JOIN PROGRAM

Apply by filling Go through a screening call An offer letter will be
a simple online with the Admission rolled out to the
application form Director’s office selected candidates

Program Fee PAYMENTS

Candidates can pay the program fee through
`2,50,000+GST
net banking, credit cards, or debit cards.

FINANCIAL AID
With our corporate financial partnerships
avail education loans at 0% interest rate* .

*Conditions Apply. Please reach out to the admissions team for more details.

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PROGRAM PARTNERS

Great Learning is a leading global ed-tech company for professional

and higher education. It offers comprehensive, industry-relevant
programs across various cutting-edge Technology, Data, and
Business domains. Great Learning's programs are developed in
collaboration with the world's foremost academic institutions such
as Stanford Executive Education, MIT Professional Education, The
University of Texas at Austin, Northwestern School of Professional
Studies, National University of Singapore, Deakin University, IIT
Madras, IIT Bombay, IIT Roorkee, IIIT-Delhi, Great Lakes Institute of
Management, and more. They are constantly reimagined and
revamped to address the dynamic needs of the industry. Great
Learning is the only ed-tech company to provide these programs in
a blended mode, classroom mode, and purely online mode, relying
on its vast network of expert mentors and highly qualified faculty to
deliver an unmatched learning experience for learners in India and
the world over. Great Learning is on a mission to enable
transformative learning and career success in the digital economy
for professionals and students across the globe and till date, we
have impacted over 6.6 million learners from over 170+ countries.

Great Lakes Executive Learning is the executive learning arm of Great Lakes
Institute of Management. Great Lakes is India’s leading business school with
campuses in Chennai and Gurgaon. Led by exceptional faculty and steered
by an outstanding advisory council, Great Lakes is ranked amongst India’s
top 10 business schools and is ranked as the best in the country when it
comes to learning Data Science and Analytics. Learning Data Science from
Great Lakes ensures you get the industry credibility and acceptance as you
look to build your career

The University of Texas at Austin’s McCombs School of Business is a premier

business school at a world class public research university. It cultivates
principled leaders and develops ideas through high-quality instruction,
experiential learning, and the pursuit of relevant, groundbreaking research,
shaping those who will shape tomorrow and solve the most challenging
problems.

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POWER AHEAD IN YOUR CAREER WITH
GREAT LEARNING.
START LEARNING TODAY.

CONTACT US
+91 80471 89252
aiml@greatlearning.in
mygreatlearning.com