PG Program in Artificial Intelligence and Machine Learning

Master Artificial Intelligence - The Next Digital Frontier

PG Program - Artificial Intelligence & Machine Learning

Online | 9 Months
Apply Now
Application Deadline - 29th Aug 2019

Why Join PGP-Artificial Intelligence and Machine Learning?

Certificate from UT Austin

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

Rated 4.6+/5

60% career transition within 6 months of program completion

Unique Mentored Learning

Customized learning in small learning groups

The University of Texas at Austin Rankings

#2

Rank
in Analytics

QS Business Analytics Rankings, 2018

#4

Rank
in Artificial Intelligence

CS Rankings

#7

Rank
in Machine Learning

CS Rankings

#8

Rank
in Artificial Intelligence

U.S. News and World Report Rankings, 2018

The PGP-Artificial Intelligence and Machine Learning program curriculum and projects have been designed in collaboration with The University of Texas at Austin - McCombs School of Business and is taught using Great Learning’s unique mentored learning model that combines a live interactive online classroom experience, support, and career coaching to ensure successful learning outcomes. The program is taught by renowned IIT faculty, experienced industry professionals and top global faculty. It builds a solid foundation by covering the most popular & widely used Artificial Intelligence & Machine Learning techniques and their application to areas such as Deep Learning, Computer Vision & Natural Language Processing. All learning is project based and hands-on to ensure practical job skills are developed.

Upon successful completion of the course, participants will receive a verified digital certificate from The University of Texas at Austin that is ranked #2 in the world for Analytics by the QS World University Rankings 2018 and #8 in Artificial Intelligence by USNews.com.

Certificate from The University of Texas

KUMAR MUTHURAMAN

Faculty Director, PGP-AIML

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

MS & PhD: Stanford University

Aritificial Intelligence and Machine Learning program introduction video

Program Structure

A structured 9-months online program with hands-on projects and learning support sessions on weekends

Online content

Best-in-class recorded content by faculty from IIT Bombay, experienced industry professionals and top global faculty.

Hands-on projects

Practical assignments, case studies and instructor-led practice sessions on data sets.

Weekend instructor led classes

64 hours of personalised mentorship from Machine Learning & Data Science professionals working in leading companies.

9 Months Exhaustive AI & ML Program

Rated 4.6+/5 | Certificate from UT Austin

Unique Mentored Learning

Personalized Attention:
Mentoring in Small Groups

Self-study online is difficult.
Our unique Mentored Learning model supports you at every step

  • 64+ hours of online mentorship
  • 150+ hours of online resources
  • Individual doubt-solving with expert mentors
  • Access online content through web and mobile app
  • Personalised feedback from industry experts
  • Dedicated Online groups to interact with your mentor
  • Regular industry sessions with experts

How Does Mentorship Work?

Personalised mentorship: The most effective way to learning online

Participants learn in small groups of 5 & every group is assigned a Artificial Intelligence & Machine Learning mentor. Every participant gets access to personalised mentorship by engaging through weekend online sessions with one of our 100+ distinguished Artificial Intelligence & Machine Learning mentors.

The best of industry and academia

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

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 100+ Mentors work at the best companies

PGP-Artificial Intelligence and Machine Learning Learning Experience

Personalized learning

Each module has recorded content that is followed by a session with one of the 100+ 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.

Industry exposure sessions

Participants get access to industry videos and webinars conducted by industry experts periodically, in addition to the usual mentoring sessions. These resources provide insights into current industry trends & real-life business problems.

Experiential learning projects

An experiential learning project at the end of every module helps candidates internalize their understanding of the content consumed. Our coursework and practical assignments are designed to enable candidates to apply what they have learned during self-study and industry sessions.

Career enhancement sessions

The program includes career development sessions which help candidates identify their strengths and empower them to clear interviews. Interacting with industry practitioners provides exposure and experience that helps them in transitioning to Artificial Intelligence & Machine Learning roles.

ePortfolio

As you complete your experential 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.

Curriculum

Foundations of AI (Released as self-paced learning before the course commencement)
  • Significant Functions
  • Packages and Routines
  • Descriptive & Inferential Stats
  • Probability & Conditional Prob
Visualization principles and techniques
Machine Learning
  • Linear regression
  • Logistic regression
  • KNN
  • Naive Baye's
  • SVM
  • Decison Tree
  • Random forest
  • Ensemble methods
  • Clustering
  • K-means clustering
  • Hierarchical clustering
  • PCA
  • Feature extraction
  • Model Defects & Evaluation Metrics
  • Model tuning
  • Popularity based model
  • Market basket analysis
  • Content based model
  • Collaborative filtering
Deep Learning
  • Neural Network Basics
  • Deep Neural Networks
  • Activation function, Loss function
  • Optimisers, Drop outs, Regularization parameters
  • Tensor Flow & Keras for Neural Networks & Deep Learning
  • Pre-processing image Data
  • Convolutional Neural Networks
  • Transfer Learning – ResNet, AlexNet, VGGNet, InceptionNet.
  • Keras library for CNNs
  • Text Extraction techniques
  • Bag of words, TF-IDF, N-Grams
  • Word2vec, GLOVE
  • RNN
  • LSTM
Languages and tools
  • Python
  • Data libraries like Pandas, Numpy, Scipy
  • Python ML library scikit-learn
  • Python visualization library Matplotlib
  • NLP library NLTK
  • Tensor Flow
  • Keras

Capstone Projects

Candidates will work on 8+ projects and a capstone project spread across topics such as Supervised / Unsupervised Learning, Ensemble Techniques, Featurization, Recommendation Systems, Neural Networks, NLP, Computer Vision.

Hackathons

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:

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

Admission Details

Eligibility

  • done Possesses an Undergraduate/Bachelor's degree with a minimum of 50% aggregate marks or equivalent
  • done Is comfortable using a programming language and is familiar with college-level mathematics and statistics

Selection Process

Step 1

Fill an application form

Interested candidates need to apply by filling up an Online Application Form.

Step 2

Shortlisting and Panel Review

A panel of 2 to 3 faculty members reviews every application in detail to identify candidates suitable for the program. They shortlist based on your profile – which comprises (but is not limited to) your undergraduate or bachelor’s stream, percentage, post qualification work experience, and relevance for the program.

Step 3

Call from Program Advisor

You can expect your Program Advisor to contact you shortly to inform you about your review result. If you have been short-listed you will receive a call from your Program Advisor to schedule your interview call.

Step 4

Interview/Screening

The shortlisted candidates then go through a telephonic interview/screening. This call will be with the Program Director for PGP-Artificial Intelligence and Machine Learning. (Interview may be waived for candidates with strong profiles and experience)

Step 5

Offer Stage

After a final faculty review, every candidate is assigned a score that includes interview and faculty score. Candidates with the highest total scores are invited to pursue the program.

Fee Details

5,000 USD
Installments
Admission Fee 1,000 USD
1st Installment 2,000 USD
2nd Installment 2,000 USD
Total 5,000 USD

Payments

Candidates can pay the program fee through

account_balance
Bank Transfer
credit_card
Credit/Debit Cards

Fee Includes

Tution Fee

Learning Material

Mentorship Sessions

Upcoming Application Deadline

29th Aug 2019

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

Apply Now

Batch starts 7th Sep 2019

Frequently Asked Questions

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 in technical role with demonstrated programming experience
  • Familiarity with college-level mathematics and statistics
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.
You will get weekly assistance from expert industry practitioners, who will clarify your doubts, and offer guidance regarding your projects.
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.
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.
We require that you have a technical background to get the most out of the program. The class will comprise of seasoned professionals, often with years of technical experience, and we have designed the program with this in mind. For those of you who are interested, and do not have a significant technical background, we offer other programs. Please contact the admissions team for more information.
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.
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.
We accept corporate sponsorships and can assist you with the process. For more information, you can write to us at aiml.utaustin@greatlearning.in
The career support activities in Post graduate program in Artificial intelligence and Machine Learning begin with helping candidates prepare for Artificial Intelligence and Machine Learning careers through sessions conducted by industry experts. We also provide our candidates and alumni with access to any opportunities that partner companies share with us.
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.
If you are interested in the Program, you can apply through the online application form. If you need assistance, please write to us at aiml.utaustin@greatlearning.in
All the candidates will be analyzing a real world problem using a range of tools & techniques that they have learned in class. The candidates will be also given access to online Labs which will help them during the project. The duration of the project is 1 to 1.5 months.
Yes, once admitted, you will receive a programming refresher (online) on Python. We strongly recommend that you complete these introductory online courses so that you are ready from day one of the program.
The candidates need to bring their own laptops; the technology requirement shall be shared at the time of enrolment.
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