India’s Most Exhaustive Machine Learning Program

PGP - Machine Learning

Weekend Classroom | Online Mentorship
Apply Now
Application Deadline 31st May 2020

Academic Partners

  • UT Austin University Logo
  • Great Lakes Logo

Why Join our PG Machine Learning Course?

faculty-icon
Get International recognition

On successful completion of the program, candidates receive certificates from The University of Texas at Austin, whose AI Program is ranked #8 globally, along with the prestigious Great Lakes Executive Learning which is ranked among Indias' top 10 Business Schools.

Great Learning online class
Flexibility of learning in class or online

In the classroom format, 120 hours of machine learning course is delivered through classroom lectures by our distinguished faculty that happen one weekend every month. In the online format, you learn from lecture videos coupled with weekly online mentorship sessions with Machine Learning experts in small groups.

Great Learning learn by doing
Learn by doing

The Machine Learning Course follows a rigorous hands-on approach where participants have to work on several real-life business problems, case studies, mini projects and capstone project. Every candidate also gets a mentor who provides guidance and mentorship for the capstone project.

Great Learning industry exposure
Corporate partnership & industry exposure

The Machine Learning Course gives the participants an opportunity to learn from some of the industry’s leading minds in Machine Learning. Industry experts participate in guest lectures, case studies and mentor candidates in their capstone projects to ensure a thorough understanding of what’s happening in the industry.

Program Experience

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 PG Machine Learning Course enables candidates to make this transition to high-growth careers in Machine Learning, Deep Learning and Data Science.

Great Learning innovative approach
Innovative approach

Our course offers a learning environment that causes minimal disruptions to your work schedule. In both the classroom and online formats sessions are conducted during the weekends and are assisted by online webinars, discussions and assignments for holistic learning. For every assignment, you will also get the model solution as a recorded walkthrough.

Great Learning industry relevant
Industry-relevant curriculum

A comprehensive Machine Learning Course that combines Data Science, Machine Learning and Deep Learning to prepare candidates for the roles of Data Scientists, Machine Learning Engineers & Architects, Technology & Solution Architects etc. The course uses a combination of learning from distinguished faculty, industry Machine Learning experts and real-life projects to enable this.

great-learning-hands-on-project
Hands-on projects & ePortfolio

Throughout the course, 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 ePortfolio. This ePortfolio is verified by Great Lakes and is a shareable document that our participants use to indicate their expertise and exposure in Machine Learning.

Great Learning talent pool
Peer Learning

The PGP-Machine Learning class consists of working professionals from excellent organizations and backgrounds with diverse experiences across industries and roles. As a participant, interactions with such a peer group will enhance your learning experience.

India's Most Exhaustive Machine Learning Course

Classroom | Online

Delivery Formats

Great Learning unique learning
Weekend Classroom | 7 month
  • 120 hours of weekend classroom sessions, one weekend every month for 7 months
  • Additional 100+ hours of online learning including self-learning content, reading material and projects
  • 8 hands-on projects and 1 capstone project under the guidance of a mentor
  • Participate in Great Learning's Machine Learning Hackathons
Great Learning online mode
Online Mentorship | 7 month
  • Learn from 50+ hours of online video content from top Machine Learning faculty
  • 2 hours of online mentorship sessions with Machine Learning experts every weekend to clear doubts and case study walk-throughs
  • 8 hands-on projects and 1 Capstone project under the guidance of a mentor

Dual Certificate & ePortfolio

Earn Dual Certificate from The University of Texas at Austin and Great Lakes . Your certificate and ePortfolio can also be shared on social media channels to establish your credibility in Machine Learning.

Great Learning - Machine Learning Certificate
UT Austin - Machine Learning Certificate
Great Learning Academic ePortfolio of Machine Learning alumnus Rajesh Mohan

Curriculum

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

Foundations
Statistical Learning
  • Statistical analysis
  • Descriptive statistics
  • Probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
Python for Data Science
  • Python Overview
  • Pandas for pre-processing and exploratory data analysis
  • Numpy for statistical analysis
  • Matplotlib and Seaborn for data visualization
Machine learning module
Supervised Learning
  • Linear Regression
  • Logistic regression
  • K-NN classification
  • Naive Bayesian classifiers
  • Support Vector Machines
Ensemble Techniques
  • Decision Trees
  • Bagging
  • Boosting
  • Random Forests
  • Stacking
Unsupervised Learning
  • K Means clustering
  • Hierarchical clustering
  • Principal Component Analysis
Featurization, Model Selection & Tuning
  • Feature extraction
  • Model defects & evaluation metrics
  • Model selection and tuning
  • Comparison of machine learning models
Recommendation Systems
  • Collaborative filtering
  • Content based filtering
  • Hybrid recommendation systems
  • Performance measurement
Introduction to Deep Learning
  • Fundamentals of neural networks
  • Tensorflow and Keras
  • Convolutional Neural Networks
  • Evaluation of Deep Learning models

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 course. 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 course.

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

E-commerce

Sentiment Score from Reviews of Amazon Products

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 & Techniques

Python, Feature Selection or Extraction, Sentiment Analysis, Text Analytics

Dataset

Amazon Review Dataset

Finance

Behavioral Pattern Recognition of Multilayer Online Role-Playing Game Players

Behavioral Pattern Recognition of Multilayer Online Role-Playing Game Players using Machine Learning and artificial neural networks

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 & Techniques

Python, Supervised Learning, Ensemble Techniques, Model Selection

Dataset

Customer Data of a Company

Music App Industry

Music Data Analysis (Recommendation and Prediction)

Analyze Lastfm (last.fm 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 & Techniques

Python, Exploration Data Analytics, Recommendation algorithms, User profiling, Prediction, Big Data Analytics

Dataset

Last FM Dataset

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
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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
  • Bangalore
Great Learning Logo
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
  • Chennai
  • Hyderabad
Great Learning - Hackathon Buddihealth
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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
  • Bangalore
Great Learning Logo

India’s Best Machine Learning Faculty

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

Hands-On Learning from AI and Machine Learning Practitioners

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

Career Support - GL Excelerate

As a participant in the Great Learning's program, GL Excelerate helps you unlock your potential, highlight your skills and connect to the right opportunities for your next job.

Great Learning interview preperation
Exclusive recruitment drives

Attend Great Learning job fairs organised every 2 months across cities. Participate in our recruitment drives with top tech companies looking for professionals like you.

great-learning-rec-drive
Access to curated jobs

Access a list of jobs relevant to your experience and domain. Leverage our dedicated career support team working with 350+ organisations, who’ll recommend the right jobs for you.

Great Learning interview
Interview preparation workshops

Familiarise yourself with commonly asked questions that’ll help you crack any technical interview. Use your ePortfolio to showcase your skills and improve your chances of getting hired.

Great Learning career mentorship
Personalised career mentorship

Get an expert career mentor personalised to your experience and industry, who will help you navigate your path to career success. Get guidance on choosing the right opportunities, building a great CV and much more.

Organizations Participating in Recruitment Drives

Great Learning Career support companies Great Learning Career support companies

Alumni Success Stories

Balaji SR

Data Scientist, Andhra Bank

The Industry exposure has helped me bridge the gap between the theory and practice. Industry exposure during the program allowed me to get to end to end knowledge right from data extraction to storytelling. I learnt about the latest industry trends, as to why a particular technique is used for a situation, challenges faced, case studies, model development and validation etc.

Clarence Wong

Data Scientist, Microsoft

The professors were extremely flexible and ready to spend more time on certain topics based on the learning requirements of the batch. The industry speakers were very knowledgeable and had hands on experience on the topics delivered by them. Interacting with them on real world problems was one of the biggest takeaways from the course.

Batch Profile

The PG Machine Learning Course class consists of working professionals from excellent organizations and backgrounds maintaining an impressive diversity across work experience, roles and industries.

Batch Industry Diversity

Learning course batch industry diversity

Batch Work Experience Distribution

Learning batch work experience distribution

The PG Machine Learning Course class comes from some of the leading organizations.

Great Learning - Artificial Intelligence and Machine Learning classes by leading organisation Great Learning - Artificial Intelligence and Machine Learning classes by leading organisation

Admission Details

Eligibility

  • done Bachelor's degree with a minimum of 50% aggregate marks or equivalent.
  • done Should also be comfortable using at least one programming language and be familiar with college-level mathematics and statistics (For candidates who do not know Python, we offer a free pre-program tutorial).
Grear Learning admission and selection process

Selection Process

Step 1

Fill application form

Step 2

Application review

Admissions committee will review and shortlist.

Step 3

Personal interview

These candidates will be invited for an interview and an offer will be made to the selected applicants.

Fee for Classroom Mode

Great Learning rupee icon 2,50,000 + GST
Installments
Admission Fee Great Learning rupee icon 50,000
1st Installment Great Learning rupee icon 66,667
2nd Installment Great Learning rupee icon 66,667
3rd Installment Great Learning rupee icon 66,666
Total 2,50,000

Fee for Online Mode

Great Learning rupee icon 1,50,000 + GST
Installments
Admission Fee Great Learning rupee icon 25,000
1st Installment Great Learning rupee icon 41,667
2nd Installment Great Learning rupee icon 41,667
3rd Installment Great Learning rupee icon 41,666
Total 1,50,000

Payments

Candidates can pay the program fee through

account_balance
Net Banking
credit_card
Credit/Debit Cards
Cheque, DD

Financial Aid

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

Great Learning education loan financial partners by Zestmoney
Great Learning education loan financial partners by Eduvanz
Great Learning education loan financial partners by HDFC

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

Upcoming Application Deadline for the PG Machine Learning Course

31st May 2020

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

Frequently Asked Questions

What is the eligibility for the Machine Learning Course?
The post-graduate program in Machine Learning is a hands-on program designed for technology professionals. The eligibility criteria are as follows:
  • Graduate with a minimum of 50% marks or equivalent
  • Familiarity with college-level mathematics and statistics
  • Comfortable with Programming
Apply Now - Machine Learning Course Demo.
What are the delivery formats available for Machine Learning Course?
The PG Machine Learning Course is available in 2 formats - Classroom based learning and Online Learning with Mentorship.
  • For the classroom format, classes will be held on one weekend each month for the 7 months coupled with Online Reading Material.
  • For the online format, mentored online sessions will be held on either a Saturday or a Sunday every weekend.
Explore Here - Machine Learning Formats.
Do I need to know programming?
Since this program is designed for technology professionals looking to work hands-on with a range of machine learning techniques most valued by the industry, you should be comfortable with programming. This may be in any programming language and not necessarily in Python, but having experience in Python is an added plus. For candidates who do not know Python, we offer a free pre-program tutorial. Explore - Machine Learning Program Details.
What is the program architecture?

The blended format of the program consists of about 220 hours of active learning – nearly 120 hours of classroom sessions (one weekend a month) that include hands-on exploration of the tools and techniques and the rest a combination of online learning, practical lab assignments and projects.

The online format of the program consists of 50+ hours of online video content from top ML faculty along with 2 hours of online mentorship sessions with ML experts every weekend to clear doubts and project assistance. Read More - Machine Learning Program Structure.

When does the program begin?
Where will the classes be conducted for the classroom format?
The classes for Gurgaon, Hyderabad, Chennai, Bangalore, Mumbai & Pune will be held at their respective Campuses/Centers in the city. Apply Now - Machine Learning Course Demo.
Will the program certificate be awarded by Great Lakes and UT Austin?
Yes. The Certificate for the Post-Graduate Program in Machine Learning will be awarded by Great Lakes Executive Learning and The University of Texas at Austin. View Here - Machine Learning Certificate.
What is the role of The University of Texas at Austin - McCombs School of Business in the PGP-ML?
The PGP-ML curriculum has been designed by Great Lakes and UT Austin-McCombs with the learning content and assessments created by faculties from Great Lakes, UT Austin and other practising data scientists and Artificial Intelligence experts.
Upon completion, all successful participants get Dual Certificate from Great Lakes and The University of Texas at Austin.
Will Great Lakes faculty be teaching this course?
Yes. Classroom sessions are delivered by Great Lakes faculty in conjunction with technology specialists with decades of Advanced Analytics and Machine learning expertise. Industry stalwarts who apply machine learning tools and techniques everyday will contribute to your learning outcomes through industry sessions, case studies and project support. Explore more about Machine Learning faculty.
Will I have to spend extra on books, online learning material or license fee?
Not at all. 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. Download - Machine Learning Program Brochure.
Will the content be available after the course is completed?
Yes. 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 one year after completion of the program. If you require extended access, please reach out to the program team. Contact Us For More Details.
How will I be evaluated during the course?
PG Machine Learning Course is a holistic and rigorous Program and follows a continuous evaluation scheme. Candidates are evaluated in the courses they undergo through case studies, quizzes, assignments or project reports. Explore More Details Here.
Can my company sponsor me for this course?
We accept corporate sponsorships and can assist you with the process. For more information, you can write to us at pgpml@greatlearning.in.
Will there be placements at the end of the course?
The career support activities at Great Lakes’ post graduate program in Machine Learning begin with helping candidates prepare for advanced analytics 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. Explore Now - Great Learning Excelerate.
What is the admission process?
You are invited to apply using our online application form. Our admissions panel evaluates all applications and you will be invited to an interview (in-person, telephone or video) if you are shortlisted. Admissions occur on a rolling basis, so you are encouraged to apply early if you’re interested. Online Application Form.
How can I apply for this course?
If you are interested in the Program, you can apply through the online application form. If you need assistance, please write to us at pgpml@greatlearning.in
Online Application Form.
How will I get access to online labs?
Online access to the lab will be provided at the start of the program. You can get access to various tools and libraries using the lab environment. Click Here - Machine Learning Curriculum.
Will there be any Financial Assistance?
The Great Lakes PG Machine Learning Course has tie-ups with HDFC Credila, Avanse Education (DHFL Group), and Zest Money for providing education loans. Check Now - Admission and Fee Details.
What is a Capstone Project?
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 3-4 months. Explore - Capstone Projects and Hackathons.
Can I brush up on my programming before we start?
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. Download Brochure.
Do candidates need to bring their own laptop?
The candidates need to bring their own laptops; the technology requirement shall be shared at the time of enrolment. Download Brochure.

Machine Learning Course by Great Learning: A Future to Rely On

Machine learning is one of the most exciting careers that you could choose. Machine learning is considered as one of the fastest growing technologies. But, what exactly is machine learning? Machine learning is a subset of artificial intelligence that renders systems the skill to spontaneously learn and progress from experience without being specifically instructed.

Why machine learning?

Machine learning has shown a great impact on many industries that have been applied such as health care, transportation, finance, logistics, etc. Machine learning is growing rapidly day by day and this offers various job roles as many industries are accommodating machine learning practices.

Importance of machine learning

The techniques used in deep learning or deep neural networks have been around for many years. These techniques weren't this effective in the early days. While in recent years, the practice of tools and techniques of machine learning has dramatically increased. Machine learning when applied to the right data results in great breakthroughs. The quality of the machine learning outputs has been extremely appreciable. Machine learning will definitely stand as a game changer in every field that is applied.

Getting the tasks done by computers such as image recognition, translation, speech recognition, etc is a major technological upgrade. Machine learning has made human lives easy. The advances in machine learning will do make a big difference in many fields where it has been applied.

What are the top machine learning practising companies in India?

Machine learning has become a necessity for almost every industry. So, there is no such set of machine learning companies that alone practice machine learning. Many top companies such as Genpact, Flipkart, Apple, Amazon, Google, etc are thoroughly applying machine learning practices.

How to become a machine learning engineer?

If you are keenly interested to become a machine learning engineer, let us learn how to start learning machine learning? Taking up the machine learning course would be the most preferable decision you could make to become a machine learning engineer. To attain a perfect understanding of all the concepts of machine learning, you got to learn machine learning from scratch which is a tough task. But there are few prerequisites that help to learn machine learning an easy task. Refer the following link to know about the various prerequisites of machine learning.

If you are yearning to start a career in machine learning and haunting the best way to learn machine learning, this page will surely lead you to find the best place to learn machine learning.

The roles and responsibilities of a machine learning engineer

Before learning about the roles and responsibilities of a machine learning engineer, first, let us understand who exactly is a machine learning engineer? A Machine learning engineer can be defined as highly skilled programmers that develop programs that help the system to analyze and program without being given specific directions.

Roles and Responsibilities of a machine learning engineer

  1. Study and Transform Data Science Prototypes

    Data science has become essential across the wide range of industries in recent years. Data science has developed a set of rules to understand human intelligence which is popularly known as artificial intelligence. The practices of artificial intelligence would help to add real value to the business.
    A machine learning engineer has to study and transform all the prototypes.

  2. Design an effective machine learning system

    Designing a production ready machine learning system is one of the major responsibilities that a machine learning engineer has. Machine learning engineers need to learn the principles of reactive design and they will have to build pipelines that are creative with highly scalable
    create a high scale

  3. Research and Implement appropriate Machine learning algorithms

    When you keenly observe machine learning algorithms there is no one particular algorithm that fits every problem. There are several factors that affect when you choose an inappropriate machine learning algorithm. Specific problems which are unique desire an indifferent approach to solving them while some other problems are open to the trial and error process.
    There are basically three types of machine learning algorithms.

    1. Supervised
    2. Unsupervised
    3. Reinforcement learning
  4. Develop Machine learning applications according to the requirements

    Machine learning results will always be based on the algorithms that a machine learning engineer generates. So, the development of machine learning applications requires a collection of advanced languages, programming tools that are accessible by the developers.

  5. Select appropriate data sets and data representation methods

    This is the most typical role played by the machine learning engineer which is also the major responsibility that does involve a lot of risks. using the data that you have collected into operation. It does require the understanding of basic mathematics and statistics besides applying the tools required for analysis such as Python, R, Matlab, etc and the tools required for visualization. It also requires the understanding of databases such as SQL.

  6. Reporting

    Making sure about the data that needs to be considered to generate algorithms. The reporting plays a major role as the data that you choose decides the output.

  7. Test and experiments

    The machine learning experiments does consume a lot of time and effort. To perform discrete experiments, you must be efficient in analyzing the reasons for the test failures. Carefully planning and organizing the type of experiments that you decide to run is the most important task.

  8. Perform statistical analysis

    Performing statistical analysis is another important role and responsibility of a machine learning engineer. Statistical and machine learning are closely related to each other. Statistical analysis helps to retrieve effective machine learning results.