Post Graduate
Program in
ARTIFICIAL
INTELLIGENCE
& MACHINE LEARNING
AI - THE NEXT
DIGITAL
FRONTIER
60% RISE IN DEMAND
for Artificial Intelligence and Machine
Learning experts in 2018*
(Kelly OCG)
40% OF DIGITAL
TRANSFORMATION
initiatives will use AI services by 2019
and by 2021, 75% of enterprise
applications will use AI*
(IDC report 2018)
$40 BILLION
was spent by companies around the
world in developing AI capabilities
in 2016*
(McKinsey Global Institute report on
Artificial Intelligence)
75%
of Indian companies feel that the
shortage of skilled professionals is
slowing down their adoption of AI*
(as per Intel/IDC)
#1
AIML PROGRAM
Source: Analytics India Magazine
WHY
GREAT
LEARNING
15000+
Students
10 Million+
Hours of Learning Delivered
15+
Top Ranked Programs
1000+
Industry Experts
25+
India’s Best Data Science
Faculty
WHAT MAKES OUR
AIML PROGRAM UNIQUE?
Covers Artificial Hands-on program As part of this program,
Intelligence & Machine using AI and ML lab you will be making all
Learning technologies and 12+ projects. It of your submissions on
and applications features case studies Github. Github is an
including Machine and learning from some online repository which
Learning, Deep of the top global helps you to store all
Learning, Computer companies like Uber, the projects and
Vision, Natural Netflix, Google, assignments you have
Language Processing, Amazon etc. done as part of this
Reinforcement program in a single
Learning, Neural For every assignment place. Today, most
Network, Tensor Flow you work as part of this companies look at
and many more. program, you will get potential recruits
to see the solutions of Github profiles to
The program is offered the assignment as check their technical
in two formats, a recorded walkthroughs. expertise before hiring
blended format Recorded walkthroughs them.
(classroom sessions help you to understand
with online content) & the concepts better Designed by leading
online only (online and analyze a problem academic and industry
videos with weekend from the view of an experts with
mentorship sessions) expert. IIT-Bombay faculty.
WHAT CAN OUR
AIML PROGRAM HELP
YOU ACHIEVE?
Develop expertise in popular Develop a verified portfolio with
AI & ML technologies and 12+ projects that will showcase the
problem-solving methodologies new skills acquired
Develop the ability to independently Build expertise in AI & ML which are
solve business problems quickly becoming the most
using AI & ML sought-after skills around the world
Learn to use popular AI & ML
technologies like Python, Tensorflow
and Keras to develop applications
CERTIFICATE
The program is internationally recognized and participants earn dual certificates from
The University of Texas at Austin and Great Lakes.
PROGRAM FORMATS:
ONLINE LEARNING
In this format, learning occurs through online videos along with online mentorship
sessions every weekend to clear doubts, reinforce concepts and for provide assistance
on projects. The mentors come with substantial industry experience which helps
learners gain an industry perspective. This guidance plays a critical role in making
them industry-ready.
75+ 150+ 1 12+ Hackathon
hours of hours of online Capstone hands-on Company
online learning (self-learning project projects sponsored
Mentor and content, reading hackathons
Industry material, assessments,
sessions projects and
assignments)
12 Months | 225+ hours of learning
CURRICULUM
FOUNDATIONS
Python for AI & ML Applied Statistics
Python Basics Descriptive Statistics
Python Functions and Packages Probability & Conditional Probability
Working with Data Structures,Arrays, Hypothesis Testing
Vectors & Data Frames Inferential Statistics
Jupyter Notebook – Installation & Probability Distributions
function
Pandas, NumPy, Matplotlib,
Seaborn
MACHINE LEARNING
Supervised learning Ensemble Techniques
Linear Regression Decision Trees
Multiple Variable Linear Regression Bagging
Logistic Regression Random Forests
Naive Bayes Classifiers Boosting
k-NN Classification
Support Vector Machines Recommendation Systems
Introduction to Recommendation
Unsupervised learning Systems
K-means Clustering Popularity based model
Hierarchical Clustering Content based Recommendation
Dimension Reduction-PCA System
Collaborative Filtering (User
similarity & Item similarity)
Hybrid Models
ARTIFICIAL INTELLIGENCE
Introduction to Neural Networks Sequential Models and NLP
and Deep Learning Introduction to Sequential data
RNNs and its mechanisms
Introduction to Perceptron Vanishing & Exploding gradients
& Neural Networks in RNNs
Activation and Loss functions LSTMs - Long short-term memory
Gradient Descent GRUs - Gated recurrent unit
Batch Normalization LSTMs Applications
TensorFlow & Keras for Neural Time series analysis
Networks LSTMs with attention mechanism
Hyper Parameter Tuning Neural Machine Translation
Advanced Language Models:
Computer vision Transformers, BERT, XLNet
Introduction to Convolutional
Neural Networks Advanced Computer Vision
Convolution, Pooling, Padding Object Detection
& its mechanisms YOLO, R-CNN, SSD
Forward Propagation & Semantic Segmentation
Backpropagation for CNNs U-Net
CNN architectures like AlexNet, Face Recognition using Siamese
VGGNet, InceptionNet & ResNet Networks
Transfer Learning Instance Segmentation
NLP Basics(Natural Introduction to GANs (Generative
Language Processing) adversarial networks)
Introduction to NLP Introduction to GANs
Stop Words Generative Networks
Tokenization Adversarial Networks
Stemming and lemmatization How GANs work?
Bag of Words Model DCGANs - Deep Convolution GANs
Word Vectorizer Applications of GANs
TF-IDF
POS Tagging
Named Entity Recognition Introduction to Reinforcement
Learning (RL)
RL Framework
Component of RL Framework
Examples of RL Systems
Types of RL Systems
Q-learning
LANGUAGES AND TOOLS
Python TensorFlow Scipy
Python ML library
Pandas Matplotlib
Scikit-learn
NLP library NLTK Numpy Keras
PROJECTS
Here is a sample set of projects which you will be working as part of this program
Analyze health information Predicting the Strength
to make decisions for of high-performance
insurance business concrete
This project uses Hypothesis Testing and Concrete is the most important
Visualization to leverage customer's material in civil engineering. The
health information like smoking habits, concrete compressive strength is a
BMI, age, and gender for checking highly nonlinear function of age and
statistical evidence to make valuable ingredients. These ingredients
decisions of insurance business like include cement, blast furnace slag,
charges for health insurance. fly ash, water, superplasticizer, coarse
aggregate, and fine aggregate.
Diagnosing Parkinson's Implementing an Image
disease using Random classification neural
Forests network to classify Street
House View Numbers
Parkinson’s Disease (PD) is a
degenerative neurological disorder Recognizing multi-digit numbers in
marked by decreased dopamine levels photographs captured at street level
in the brain. It manifests itself through is an important component of
a deterioration of movement, including modern-day map making. A classic
the presence of tremors and stiffness. example of a corpus of such
There is commonly a marked effect on street-level photographs is Google’s
speech, including dysarthria (difficulty Street View imagery composed of
articulating sounds), hypophonia hundreds of millions of geo-located
(lowered volume), and monotone 360-degree panoramic images. The
(reduced pitch range). Additionally, ability to automatically transcribe an
cognitive impairments and changes in address number from a geolocated
mood can occur, and the risk of patch of pixels and associate the
dementia is increased. transcribed number with a known
street address helps pinpoint, with a
Traditional diagnosis of Parkinson’s high degree of accuracy, the location
Disease involves a clinician taking a of the building it represents.
neurological history of the patient and
observing motor skills in various More broadly, recognizing numbers
situations. Since there is no definitive in photographs is a problem of
laboratory test to diagnose PD, interest to the optical character
diagnosis is often difficult, particularly recognition community. While OCR
in the early stages when motor effects on constrained domains like
are not yet severe. Monitoring the document processing is well studied,
progression of the disease over time arbitrary multi-character text
requires repeated clinic visits by the recognition in photographs is still
patient. An effective screening highly challenging. This difficulty
process, particularly one that doesn’t arises due to the wide variability in
require a clinic visit, would be the visual appearance of text in the
beneficial. Since PD patients exhibit wild on account of a large range of
characteristic vocal features, voice fonts, colors, styles, orientations, and
recordings are a useful and character arrangements. The
non-invasive tool for diagnosis. If recognition problem is further
machine-learning algorithms could be complicated by environmental
applied to a voice recording dataset to factors such as lighting, shadows,
accurately diagnosis PD, this would be secularities, and occlusions as well as
an effective screening step prior to an by image acquisition factors such as
appointment with a clinician resolution, motion, and focus blur.
Face Mask Prediction Sentiment Analysis
using U-Net using LSTM
Build a deep learning model using Word embedding is a type of word
U-Net as architecture that will learn representation that allows words with
the pixel mapping of the face in an similar meaning to have a similar
image. representation. It is a distributed
representation for the text that is
We will be using transfer learning. perhaps one of the key breakthroughs
We will use the MobileNet model for the impressive performance of
which is already trained to detect deep learning methods on
the face attributes. We will need to challenging natural language
train the last 6-7 layers and freeze processing problems. We will use the
the remaining layers to train the IMDb dataset to learn word
model for predicting the mask on embedding as we train our dataset.
the face. To be able to train the This dataset contains 50,000 movie
MobileNet model, we will be using reviews from IMDB, labeled with a
the WIDER FACE dataset for various sentiment (positive or negative).
images with a single face and
multiple faces. The objective of this project is to
build a text classification model that
analyses the customer's sentiments
based on their reviews in the IMDB
database. The model uses a complex
deep learning model to build an
embedding layer followed by a
classification algorithm to analyze the
sentiment of the customers.
Deep learning (CV/NLP) Machine learning -
- Pneumonia Detection Prediction of the house
& Automatic Ticket prices
Classification
This project involves using various
The capstone project is a focused features variables available in the
approach to attempt a real-life Innercity house price dataset.
challenge with the learnings from Different Machine learning models
the program. The AIML capstone like Regression, Ensemble techniques
problems are classified under the were used to analyse and predict the
themes of Computer Vision and house prices. Also, the grid search
Natural Language Processing. The algorithm was used to tune different
Goals of the Projects achieved are parameters associated with models.
tagged here.
Face Recognition
Computer Vision: Pneumonia
Detection - Locate the position of Recognize, identify, and classify faces
inflammation in an image. within images using CNN and image
Natural Language Processing: recognition algorithms. In this
Automatic Ticket Allocation - Build hands-on project, the goal is to build
a classifier that can classify the a face recognition system, which
tickets by analysing text includes building a face detector to
locate the position of a face in an
image and a face identification model
to recognize whose face it is by
matching it to the existing database
of faces.
Classifying silhouettes Product
of vehicles Recommendation
System
The purpose is to classify a given
silhouette as one of three types of Online E-commerce websites like
vehicles, using a set of features Amazon, Flipkart uses different
extracted from the silhouette. The recommendation models to provide
vehicle may be viewed from one of different suggestions to different
many different angles. users. Amazon currently uses
item-to-item collaborative filtering,
Identifying potential which scales to massive data sets and
customers for loans produces high-quality
recommendations in real-time.
This case is about a bank (Thera
Bank) whose management wants to
explore ways of converting its
liability customers to personal loan
customers (while retaining them as
depositors). A campaign that the
bank ran last year for liability
customers showed a healthy
conversion rate of over 9% success.
This has encouraged the retail
marketing department to devise
campaigns with better target
marketing to increase the success
ratio with a minimal budget.
FACULTY
DR. KUMAR MUTHURAMAN
H. Timothy (Tim) Harkins Centennial Professor
University of Texas at Austin
PROF. MUKESH RAO
Faculty, Machine Learning
Great Learning
DR. D NARAYANA
Faculty, AI and Machine Learning
Great Learning
PROF. ABHINANDA SARKAR
Academic Director
Great Learning
DR. AMIT SETHI
Faculty
IIT Bombay
DR. ARJUN JAIN
Adjunct Faculty Member, Department of
Computational and Data Sciences
IISc
Faculty has contributed to
program curriculum and online
learning content only
TESTIMONIALS
MANISH KUMAR
Senior Engineer
Tata Consulting Engineers 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
exercises and projects at the end of
the module are really helpful in gaining
confidence.
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.
GREAT ALUMNI WORK IN
LEADING COMPANIES
ADMISSION Selection Process
DETAILS
1
Eligibility Interested candidates need to
Applicants should have a Bachelor's apply by filling a simple online
degree with a minimum of 50% aggregate application form
marks or equivalent and familiarity with
programming. For candidates who do not
know Python, we offer a free pre-program
tutorial.
2
The admissions committee and
Fee
faculty panel will review the
`2,40,000 + GST application, followed by a
screening call to shortlist eligible
candidates
Payments
Candidates can pay the program fee
through
3
Net Banking
Offer will be made to
Credit Cards or Debit Cards selected applicants
Financial aid
With our corporate financial partnerships Location
avail education loans at 0% interest rate*.
The Program is available at
BANGALORE HYDERABAD
CHENNAI PUNE
*
Conditions Apply. Please reach out to the admissions team
for more details. GURGAON MUMBAI
PROGRAM PARTNERS
The University of Texas—Austin is one of the largest
schools in USA. It was founded in 1883. Today UT Austin is
a world-renowned higher education, research-intensive
institution, serving more than 51,000 students annually
with a teaching faculty of around 3,000. University of Texas
at Austin is ranked #2 worldwide for Business Analytics
according to the QS University rankings, #2 for science,
technology, engineering and math (STEM) professionals
according to Forbes and ranked #8 in Artificial Intelligence
by the U.S.News & World Report Rankings 2018.
Great Lakes mission is to become a Center of Excellence in
fostering managerial leadership and entrepreneurship in
EXECUTIVE LEARNING
the development of human capital through quality
research, teaching, residential learning and professional
management services.
Great Learning's mission is to enable career success in the
Digital Economy. It’s programs always focus on the next
frontier of growth in industry and currently straddle across
Analytics, Data Science, Big Data, Machine Learning,
Artificial Intelligence, Deep Learning, Cloud Computing
and more. Great Learning uses technology, high-quality
content, and industry collaboration to deliver an immersive
learning experience that helps candidates learn, apply, and
demonstrate their competencies. All programs are offered
in collaboration with leading global universities and are
taken by thousands of professionals every year to secure
and grow their careers.
Learn more about the program
+91-8448480528 aiml@greatlearning.in greatlearning.in
