Learning Experience during COVID-19
Despite the global impact of COVID-19, our online learning experience remains uninterrupted
You can continue pursuing your career goals in a safe online learning environment.
For questions and support, Contact Us
Master Artificial Intelligence - The Next Digital Frontier

PG Program - Artificial Intelligence & Machine Learning

Online | 6 Months
Apply Now
Application Deadline 28th May 2020
UT Austin University Logo

#2nd worldwide

Business Analytics, QS Rankings (2018)

Why Join PGP-Artificial Intelligence and Machine Learning?

Great Learning gl certificate

Certificate from UT Austin

Ranked #2 worldwide in Analytics and Ranked #8 in Artificial Intelligence

Great Learning rating icon

Rated 4.6+/5

60% career transition within 6 months of program completion

Great Learning job opportunity

Unique Mentored Learning

Live and interactive learning in small groups

The University of Texas at Austin Rankings


in Analytics

QS Business Analytics Rankings, 2018


in Artificial Intelligence

CS Rankings


in Machine Learning

CS Rankings


in Artificial Intelligence

U.S. News and World Report Rankings, 2018

The Post Graduate Program in Artificial Intelligence and Machine Learning has been designed in collaboration with McCombs School of Business at The University of Texas at Austin, and delivered by Great Learning. It uniquely combines a comprehensive curriculum, interactive mentored learning, hands-on training, and career guidance from industry practitioners to enable successful learning and career outcomes.

Taught by renowned faculty from UT Austin, experienced professionals and global academicians; the curriculum covers the most popular and widely-used AI and Machine Learning techniques and their applications to areas such as Deep Learning, Computer Vision, Natural Language Processing, and Neural Networks. All learning is project-based and hands-on to ensure that practical, industry-relevant skills are developed.

On successful completion, candidates are awarded a certificate from The University of Texas at Austin.

Certificate from The University of Texas

All certificate images are for illustrative purposes only. The actual certificate may be subject to change at the discretion of the University.


Faculty Director, PGP-AIML

H. Timothy (Tim) Harkins Centennial Professor Faculty Director, Center for Research and Analytics

MS & PhD: Stanford University

Program Structure

A structured 6-month online program with hands-on projects and mentor-led sessions on weekends

Great Learning program structure-1

Recorded Online Lectures

Access best-in-class recorded content from UT Austin faculty, global academicians and accomplished industry experts.

Great Learning program structure-2

Hands-on Projects

Apply your skills in real-time as you work end-to-end on 8+ hands-on projects, focused on real-world business problems.

Great Learning program structure-3

Weekend Mentor-led Sessions

Practice on data sets and gain practical insights from leading data science & analytics professionals in live sessions.

6-Month Exhaustive AI & ML Program

Rated 4.6+/5 | Certificate from UT Austin

Unique Mentored Learning

Personalized Attention:
Mentoring in Small Groups

Self-learning online can be difficult.
Our mentored learning model brings you 2 hours of live and interactive mentor-led sessions every weekend in groups of 10-15 learners (micro-class).

Mentors are:

  • Professionals working in leading global companies.
  • Industry experts with several years of domain experience.
  • Matched to your domain and experience level.
  • A valuable means of networking and gaining career insights.

How Does Mentorship Work?

Great Learning Group
Personalised mentorship: The most effective way to learn online

Participants learn in small groups of 10-15 & every group is assigned an Artificial Intelligence & Machine Learning mentor. These distinguished mentors are current practitioners of AI and Machine Learning in leading global companies, and share their unique insights in live and interactive sessions every week.

Great Learning online session
The best of industry and academia

Participants learn from digital content created by some of the world’s top rated Artificial Intelligence & Machine Learning faculty. On weekends, participants are led by their mentors as they work on data sets, projects and real-world problems in Artificial Intelligence & Machine Learning.

Great Learning mentors inspire
Career coaching helps you connect the dots

Mentors inspire and guide participants at each step of their learning. Our mentors go beyond concept walkthroughs, doubt clearing and projects to help participants achieve their learning goals and also support them in their career transition in Artificial Intelligence & Machine Learning.

Mentoring is interactive and happens in small groups

Our Mentors work at the best companies

PGP-Artificial Intelligence and Machine Learning Experience

Great Learning reasons-1
Personalized Learning

Each module has recorded content that is followed by a session with one of our distinguished industry mentors, in a small group of participants. These mentors are thought leaders in different domains with several years of industry experience that enables them to impart practical knowledge and real-world insights.

Great Learning reasons-5
Webinars with UT Austin Faculty

Participate in monthly webinars with leading UT Austin faculty to learn the applications of AI & ML to different industries through case studies and live examples.

Great Learning capstone project
Experiential Learning Projects

Interspersed throughout the program, the 8+ hands-on projects help you learn the real-world applications of concepts by applying them to real-world business problems.

Great Learning reasons-6
Career Enhancement Sessions

Prepare for a successful career transition through career guidance by industry practitioners, focusing on resume and LinkedIn profile review, interview preparation and 1:1 career coaching.


As you complete your experiential projects, we will automatically create a document to showcase your learning & projects in a snapshot that we call an ePortfolio. This can also be easily shared on social media channels to establish your credibility in Artificial Intelligence & Machine Learning with potential employers.


Python for AI
  • Introduction to Python
  • NumPy, Pandas
  • Exploratory Data Analysis
  • Matplotlib, Seaborn
  • Project 1
Supervised Learning
  • Intro to Machine Learning
  • Linear Regression
  • Logistic Regression
  • Model Evaluation
  • Project 2
Ensemble Techniques
  • Decision Trees
  • Ensemble Methods - Bagging, Boosting and Stacking
  • Random Forest
  • Project 3
Feature Engineering, Model Selection and Tuning
  • Feature Engineering.
  • Sampling and Smote, Regularization
  • Pipelining
  • Model Performance Measures
  • Project 4
Unsupervised Learning
  • K-Means Clustering
  • Hierarchical Clustering
  • Project 5
Neural Networks
  • Basics
  • Activation Function, Loss Function
  • Optimizers, Regularization-Drop-outs
  • TensorFlow
  • Keras
  • Project 6
Computer Vision
  • Business Applications of Computer Vision
  • Working with Images
  • Convolutions, VGGNet
  • Transfer Learning
  • Project 7
Natural Language Processing
  • Business Applications of NLP
  • Text Extraction Techniques and Text Pre-processing
  • GLoVe, Word2Vec, Word Embeddings, POS Tagging
  • RNNs, LSTMs
  • Project 8
Self-Paced Modules
Statistical Learning
  • Descriptive Statistics
  • Inferential Statistics
  • Probability & conditional probability
  • Hypothesis Testing
  • Chi-square & ANOVA
  • Project 9
Recommendation Systems
  • Popularity-based Model
  • Market Basket Analysis
  • Content-based Model
  • Collaborative Filtering
  • SVD Approach
  • Hybrid Recommendation Systems
  • Project 10

Hands-On Projects

Candidates will work on 8+ projects spread across topics such as Statistical Learning, Supervised / Unsupervised Learning, Ensemble / Reinforcement Techniques, Neural Networks, NLP, Computer Vision, etc.


Participate in company-sponsored hackathons and establish your expertise. Apply your new skills to solve real-world problems. Here are some of our recent hackathons.
Card image cap
Hackathon: 02nd Feb 2019

HR Analytics

The problem statement is to build a machine learning model to classify salary range of different employees based on employer information.

12 Teams
Card image cap
Hackathon: 19th Jan 2019

Medical Speciality

Build a Machine Learning model which can extract information from medical prescriptions and can identify keywords to identify the medical domain of the problem which the patient is suffering from.

19 Teams
Card image cap
Hackathon: 08th Sep 2018

Ad-channel Marketing

The challenge is to build a machine learning algorithm based on 1 million click data over 4 days from an advertisement that predicts whether a user will download an app after clicking on the advertisement.

10 Teams

Learn from the Best

Learn from leading academicians in the field of Artificial Intelligence and Machine Learning and several experienced industry practitioners from top organizations. An indicative list of Artificial Intelligence and Machine Learning experts engaged with us include:

Hands-On Learning from Artificial Intelligence and Machine Learning Practitioners

Get invaluable input from the who's who of the industry:

6-Month Exhaustive AI & ML Program

Rated 4.6+/5 | Certificate from UT Austin

PGP-Artificial Intelligence and Machine Learning alumni work for world-class companies

Admission Details


  • done An undergraduate/bachelor’s degree, with a minimum of 50% aggregate marks or equivalent.
  • done Familiarity with college-level mathematics and statistics.
  • done Prior programming experience is preferred.
Grear Learning admission and selection process

Selection Process

Step 1

Application Form

Step 2

Call from your Program Advisor

A program advisor is assigned to assist you through the selection process and answer all your questions.

Step 3

Shortlisting and Panel Review

A panel will review your application to determine your fit for the program. The panel will evaluate you on your work experience, past academic credentials and motivation for joining the program.

Step 4


If shortlisted, you will go through a telephonic interview (this may be waived for candidates with strong profiles and experience).

Step 5

Admissions Offer

After a final admissions committee and faculty review, if selected, you will receive an ‘offer of admission’ letter for a seat in the upcoming cohort of the program.

Fee Details

Reach out to your Program Advisor for flexible payment plans

3,500 USD

Pay upfront and get a discount of 175 USD

3,325 USD


Candidates can pay the program fee through

Credit/Debit Cards
Bank Transfer

Fee Includes

Great Learning tution fee

Tuition Fee

Great Learning material

Learning Material

Great Learning mentorship session

Mentorship Sessions

Upcoming Application Deadline

28th May 2020

We follow a rolling admission process and admissions are closed once the requisite number of participants enroll for the upcoming cohort. So, we encourage you to apply early to secure your seat.

Apply Now

Cohort starts To be announced

Frequently Asked Questions

What is the eligibility for the program?
The post-graduate program in Artificial Intelligence and Machine Learning is a hands-on program designed for technology professionals. The eligibility criteria are as follows:
  • Minimum of 3 years of work experience. Prior programming experience is preferred.
  • Familiarity with college-level mathematics and statistics.
What will my learning schedule be like?
You can learn concepts online with recorded sessions, and clarify your doubts at the end of the week with online mentoring sessions with an industry expert.
What kind of learning assistance will I get?
You will get weekly assistance from expert industry practitioners, who will clarify your doubts, and offer guidance regarding your projects.
Who are the mentors as part of this program?
You can find the details of the mentors in the program page. Suffice to say, the mentors are industry practitioners with leading organizations and come with extensive experience in their fields.
Will I have to spend more on other material?
All required content are included in the program fee and available at any time during (and after completion of) the program. You are welcome to purchase additional material for your own reference on faculty recommendation.
I am not from a technical background - Can I join?

The course is not just for people in technology and IT but caters to professionals from all domains of the industry who wish to build a tech-based career in artificial intelligence and machine learning. However, please note that to derive value from the course, you need to have some prior experience in programming.

Will the content be available after the program is completed?
We believe that learning is continuous and hence all learning material – lecture notes, online content and supporting material – will be available through the online platform for 3 years after completion of the program.
How will I be evaluated during the program?
In this holistic and rigorous program, you will be evaluated continuously. All quizzes, assignments, attendance and projects are used to evaluate and monitor your progress towards the desired learning outcomes.
Can my company sponsor me?
What kind of career support can I expect from this program?
The PGP-Artificial Intelligence and Machine Learning offers career support to ensure you derive not just positive learning outcomes, but also the career outcomes you desire for your professional journey. You can expect career guidance through 1:1 career coaching sessions with industry practitioners, resume and LinkedIn profile review, interview preparation sessions, and webinars with UT Austin faculty.
What is the admission process?
You are invited to apply using our online application form. Our admissions panel evaluates all applications and if shortlisted, you will be required to attend an Admissions Screening Interview.
How can I apply for this program?
Do candidates need to bring their own laptops?
The candidates need to bring their own laptops; the technology requirement shall be shared at the time of enrolment.