Post Graduate Program in Machine Learning (PGP-ML)

6 month | 220+ hours | Weekend Classroom and online
Bangalore | Chennai | Hyderabad | Gurgaon | Pune | Mumbai

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Why PGP - Machine Learning

India's Most Exhaustive Machine Learning Program

6 month PG Program in Machine Learning with hands-on lab, case studies and industry applications from top ranked faculty and Machine Learning experts.

Be Industry ready

Build expertise in high-demand skills and tools such as NLP, Clustering, Python etc

Career Assistance

Propel your career by availing career assistance services like resume reviews, mentorship by industry / alumni and access to job opportunities

6 Top Reasons to Join PGP - Machine Learning (PGP-ML)

PGP-ML is a 6-month comprehensive program that combines Data Science, Machine Learning and Deep Learning to prepare candidates for the roles of Data Scientists, Machine Learning engineers, Machine Learning architects, Technology architects, Solution Engineers, Technology Consultants, Chief Technology Officers etc.

Learn from India’s best

Great Lakes faculty have been ranked amongst India’s top 10 Data Dcience and Machine Learning faculty. Most of our faculty have significant industry experience in Data Science and Machine Learning and are alumni of marquee institutions such as Harvard, Stanford, Kellogg, University of Chicago, IIMs, and IITs.

Industry Exposure

The program gives the participants an opportunity to learn from some of the Industry’s leading minds in Machine Learning. Guest Lectures, Case studies and Problem Walkthroughs ensure that participants get an understanding of what’s happening in the industry. Every candidate gets a mentor who provides guidance and mentorship for capstone project which further enhances the learning experience.

Learn by doing

The program follows a rigorous hands-on approach where participants have to work on several challenging problems, case studies, mini projects and capstone project. By working on problems, candidates get to solve industry like problems which makes them industry ready by the time they complete the program. Every candidate also gets to showcase his/her expertise through a unique Machine Learning ePortfolio – which is a collection of all projects done by the candidate, assessed and verified by Great Lakes.

Mix of classroom and online learning

The program spans 220+ hours of content in Machine Learning. Out of this, 120 hours are delivered through in-person weekend classroom sessions. Another 100+ hours of learning happens in the online mode where candidates learn from recorded content, reading material shared by faculty, projects, assignments and webinars. The classroom schedule is drawn in such a manner that it causes minimal disruption to one’s work schedule.

Corporate Partners

Great Lakes PGP-Machine Learning is designed and delivered in collaboration with the industry. Through industry experts who participate in classroom lectures and also mentor candidates for capstone projects, participants pursuing PGP-ML program get to learn from the best of academia and industry.

Capstone Projects

The capstone project is a mandatory application-oriented industry project undertaken by all candidates to develop the acumen to solve real-life business problems on Machine Learning. Our participants have often hailed the capstone project as one of the hallmarks of the learning experience because of the quality of work they get to do and also the mentorship that they receive through Great Lakes faculty and industry experts.

A Unique Learning Experience Awaits You.

Machine learning is one of the most exciting careers to build right now. There is a high demand for Machine Learning professionals in the industry but it is crucial to have relevant skills, exposure and academic pedigree to be able to capitalise on these opportunities. The Post Graduate Program in Machine Learning enables candidates to make this transition to high-growth careers in Machine Learning, Deep Learning and Data Science.

PGP-ML is a unique learning experience crafted keeping in mind the learning needs of professionals and their career aspirations. The program uses a combination of learning methods that include classroom teaching, self-learning through videos and reading materials, team-based problem solving and sessions with industry experts. Classes are conducted on one weekend every month (for 6 months) and assisted by online webinars, discussions, and assignments. Candidates can access the course content online even after they have graduated.

Online Machine Learning Lab

All the technology tools for data ingestion, processing and analysis will be stably installed, maintained and hosted for you to access at any time for assignments and the Capstone project. Candidates will get trained on Machine Learning techniques, Python (Pandas, Numpy, Scipi), Matplot Lib, Seaborn etc

Machine Learning e-Portfolio

Throughout the program, candidates would be working on multiple mini-projects, capstone project and hackathon. All this hands-on work gets captured in every candidate’s Machine Learning e-portfolio. This e-portfolio is verified by Great Lakes and is a shareable document that many of our alumni and participants use to indicate their expertise and exposure in Machine Learning. For most of our candidates and alumni who achieve career transitions, we have seen that their individual e-portfolios were often the conversation starters that finally led to their career growth.

Learn from India’s best

Since 120 hours of the program is delivered through classroom lectures, candidates get the opportunity to learn and interact in-person with top ranked Machine Learning faculty and experts. Engaging in classroom discussions and learning along with peers from the industry enables a much richer learning experience. Program participants also get to benefit from networking with industry guests and their other batch-mates as these connections often open doors to more exciting opportunities

Program Structure

Here are some of the key highlights of the program:

Program Name Post Graduate Program in Machine Learning (PGP-ML)
Certificate Great Lakes Certificate
Duration 6 Months | 15 Days of Classroom Sessions (Weekends)
  • 120 hours of classroom learning, industry lectures andML lab
  • 100+ hours of online learning (self-learning content, reading material, assessments, projects and assignments)
  • 4 hands-on projects on Machine Learning lab
  • 1 Capstone project
  • Hackathons




  1. Statistical analysis concepts
  2. Descriptive statistics
  3. Introduction to probability and Bayes theorem
  4. Probability distributions
  5. Hypothesis testing & scores
  6. Hands-on project using Python


  1. Python Overview
  2. Pandas for Pre-Processing and Exploratory Data Analysis
  3. Numpy for Statistical Analysis
  4. Matplotlib for Data Visualization
  5. Case Studies and careers
  6. Hands-on session



  1. Introduction to machine learning
  2. Supervised Learning concepts
  3. Linear Regression
  4. Logistic regression
  5. K-NN classification
  6. Naïve Bayesian classifiers
  7. SVM - (Support Vector Machines)
  8. Hands-on project using Python


  1. Decision Tree
  2. Introduction to Ensemble learning
  3. Different Ensemble Learning Techniques
  4. Bagging
  5. Boosting
  6. Random Forests
  7. Stacking
  8. Hands-on project using Python


  1. Unsupervised Learning concepts
  2. Clustering approaches
    • K Means clustering
    • Hierarchical clustering
  3. PCA (Principal Component Analysis) and its Applications
  4. Hands on project using Python


  1. Text Analytics
  2. Feature extraction
  3. Model Defects & Evaluation Metrics
  4. Model selection and tuning
  5. Comparison of machine learning models
  6. Hands-on project using Python


  1. Introduction to Recommendation Systems
  2. Types of Recommendation techniques
    • Collaborative Filtering
    • Content based Filtering
    • Hybrid RS
  3. Case Study
  4. Performance measurement
  5. Hands-on project using Python


  1. Introduction to Deep Learning Concepts
  2. Fundamentals of neural networks
  3. Introduction to Tensorflow and Keras as Deep Learning frameworks
  4. TensorFlow illustrative example
  5. Introduction to CNN
  6. Evaluation of Deep Learning models
  7. Hands on using Python

India’s best Machine Learning faculty

The faculty pool of the program consists of leading academicians in the field of data analytics along with several experienced industry practitioners from leading organizations.

Dr. Bappaditya Mukhopadyay

Professor, Analytics & Finance
Data Science and Machine Learning

Dr. P K Vishwanathan

Professor, Analytics & Operations
Data Science and Machine Learning

Dr. Sridhar Telidevara

Professor, Business Analytics
Data Science

Dr. Narayana Darapaneni

Professor, Big Data and Machine Learning
Machine Learning

Mukesh Rao

Faculty, Machine Learning
Machine Learning


Program Advisors

The program curriculum has been created in collaboration with some of the brightest minds in Machine Learning.

Satya V. Nitta

Worldwide Leader & Program Director - Cognitive Sciences and Education Technology, IBM

Ullas Nambiar

Head Of Technology Innovation at Zensar Technologies

Mr Mayur Datar

Chief Data Scientist, Flipkart


Corporate Partners

The PG Program in Machine Learning is created and delivered in collaboration with an impressive array of Corporate Partners, who contribute to making this program industry-oriented through practical instruction, real-world case studies and expert mentorship.


Capstone - The Cornerstone

The Capstone project is the seminal project that has to be done by every candidate and captures the entire learning in the program. The projects are co-created with industry experts and represent challenges and problems similar to the ones the industry solves using Machine Learning. Through the capstone project, candidates are able to experience an end-to-end problem solving experience in Machine Learning. All capstones projects are mentored by Industry experts or Great lakes faculty and the candidates are required to present their capstone projects to an evaluation committee at the end of the program.

Here are few of the recent capstone projects done by our participants in Machine Learning:

Sentiment Score from reviews of Amazon Products

  • Domain - Ecommerce
  • Description - This project involves building a machine learning model which can predict the sentiment score from the text available in Reviews dataset from Amazon website.
  • Extract suitable features from the unstructured text and apply the machine learning model to get the sentiment score to understand people’s sentiments, attitudes, or emotions towards certain Amazon product.
  • Tools - Python
  • Techniques- Feature Selection or Extraction, Sentiment Analysis, Text Analytics
  • Dataset - Amazon Review Dataset

Behavioral Pattern recognition of Multiplayer Online Role-Playing Game players using Big Data and Machine Learning and Artificial Neural Networks

  • Domain - Finance
  • Description - The project involves identification of potential customers for the personal loan product of a bank. Ensemble modeling techniques are leveraged to predict the propensity of a prospect to purchase the loan. This enables the bank to devise targeted marketing campaigns to increase the conversion rate with minimal budget.
  • Tools - Python
  • Techniques- Supervised Learning, Ensemble Techniques, Model Selection
  • Dataset - Customer Data of a Company

Music Data Analysis (Recommendation and Prediction)

  • Domain - Music App Industry
  • Description - Analyze Lastfm ( website) music dataset and apply user based collaborative filtering and other Recommendation algorithms to identify less-known Artist and new artists, enabling user to explore new songs music based on their taste.Also build a prediction model to predict whether a user will listen to an artist’s songs and frequency of listening for a particular song.
  • Tools - Python, Spark
  • Techniques- Exploration Data Analytics, Recommendation algorithms, User profiling, Prediction, Big Data Analytics
  • Dataset - Last FM Dataset

Admission Details for the PG Program - Machine Learning

Following are the eligibility criteria and selection process for the PG Program in Machine Learning


Applicants should have a bachelor's degree in Engineering, Computer Science or Mathematics/Statistics with a minimum of 50% aggregate marks or equivalent. Applicants must have at least 3 years of full-time work experience. They should also be comfortable using at least one programming language and be familiar with college-level mathematics and statistics.

Selection Process

Interested candidates need to apply by filling up an Online Application Form. The Admissions committee and faculty panel will review all the applications and shortlist candidates based on their profiles. These candidates will be invited for an interview and an offer will be made to the selected applicants.

*Admissions will be closed once the requisite number of candidates have been admitted into the program.


Admission Details for the PG Program in Machine Learning (PGP-ML)

Following are the payment details for the program:


The course fee is:

Bangalore, Chennai, Gurgaon, Hyderabad, Pune - INR 2,25,000 + GST.

Candidates can avail financial assistance and Zero % monthly EMIs. For information on easy payments options, please contact our admissions office at +91 8448480525.


Candidates can pay the program fee through Cheque, DD, Net Banking, Credit Cards or Debit Cards.*

*Great Learning does not accept cash payments and issues receipts for all fee payments made towards all our programs.

Financial aid

Selected students can contact the Admissions Office for assistance in applying for loans after receiving the offer of admission. We have several registered lending partners including HDFC Credila, Avanse, Zest Money, Incred etc. We ensure financial constraint is not a hindrance in the path of learning.


Mark the Dates

Here are the Application Deadlines for the program

Upcoming Application Deadline: 18th October 2018

Batch commencement dates


30th November 2018


16th November 2018


19th October 2018


December 2018


23rd November 2018


December 2018