88 hrs Instructor Hours
120+ Assignments
4 Guided Projects
2 Mentored Hackathons

Program Overview

The Immersive Machine Learning Bootcamp will turn you in a Machine Learning Pro. Complete with hackathons and guided projects, this program gives you a firm grounding in Machine Learning - from basics to applications.

Program Outcomes

  • Use Python for Data Science
  • Summarize data for analysis
  • Evaluate data science problems
  • Build machine learning algorithms and models.

"Amazing course for Data science and Machine Learning
The curriculum is very well thought of and has a good flow . Some of the lecturers were truly amazing . I really liked the full stack data science engineering program and will recommend it to anyone who wants to start Thier career in data science"

Arun Kutty
Specialist Master/Architect at Deloitte Digital

Curriculum

In Cricket League teams representing Indian cities contend each year. Chris Gayle is the highest run scorer in Cricket League. Do you know who is the second highest run scorer (without using ‘for’ loop)? This module can help you determine the second highest run scorer by manipulating large data sets to extract business insights

This project challenges you to manipulate large datasets without using conventional programming techniques to extract business insights

  • Basic Python Programming constructs

    An introduction to the basic concepts of Python. Learn how to use Python both interactively and through a script.

  • Functions and OOP in Python, NumPy basics

    Learn to work with the NumPy array, a faster and more powerful alternative to the list, and take your first steps in data exploration.

  • Learn Pandas basic concepts, work with Series

    Learn about the Pandas DataFrame, the superior alternative to the Python list and dictionary built on NumPy, and the de facto standard to work with tabular data in Python.

  • Work with Pandas DataFrame (Advanced)

    You will learn how to tidy, rearrange, and restructure your data using versatile pandas DataFrames.

  • Working with Visualizations

    You will learn to build various types of plots and to customize them to make them more visually appealing and interpretable.

Tech Stack

git github Commit.Live Python

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this dataset will help you determine aspects which influence price of a property other than sq.ft. area and locality.

This project challenges you to predict the final price of each home.

  • Introduction to Machine Learning

    Learn to convert a Business problem to a Machine Learning problem. # The problem: I want to predict the price of a house in NY. How to do it?

  • Descriptive Statistics

    Summarize the Housing Price dataset

  • Inferential Statistics

    Learn the Art of Statistical Inference and draw conclusions from your data.Find if raising the price of a House caused a meaningful drop in sale.

  • Exploratory Data Analysis

    Make sure you know what is the question you are trying to answer and form a hypothesis prior to jumping to ML. Visualize the data to gain further insights about the dataset.

  • Linear Regression

    Make your first prediction with our favourite ML algorithm!

Tech Stack

scikit-learn Matplotlib

Lending Club is a peer-to-peer lending company, headquartered in San Francisco. Through personal, auto refinancing loans and medical financing. Lending Club offers the borrowing and investing solution right for you.

Build a machine learning model that can reliably predict if a loan will be paid off or not and does a good job of filtering out high percentage of loan defaulters.

  • Advanced Linear Regression

    Improve the predictive power of your linear regression using Regularization techniques

  • Feature Engineering

    Be creative , learn the magic of transforming your feature set for best model outcomes

  • Feature Selection

    Improve the quality of your model by selecting the relevant features, rejecting redundant and noisy features

  • Logistic Regression

    Take the basic classification challenge - Learn to predict if your wife is angry with you or not - the ML way!

  • Decision Trees

    Use this supervised learning algorithm where we first construct the tree with historical data, and then use it to predict an outcome

Tech Stack

scikit-learn scipy numpy

The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems. The MNIST database contains 60,000 training images and 10,000 testing images. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike.

This project challenges you to identify handwritten digits images from a dataset of tens of thousands images.

  • Challenges in Machine Learning

    Learn how to handle some of the practical challenges faced in solving a ML problem, and how to deal with them

  • Clustering/ k-means

    Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy

  • Mentored Hackathon

    Over the last weekend of the training, cohort will work on some use cases and go full mile on using machine learning for problem solving.

  • Time Series

    Analyze the trends in data and forecast the future trends

  • Natural Language Processing

    Learn to extract insightful information from text data

  • Intro to Recommender Systems

    Improve user experience by recommending them the right products

Tech Stack

scikit-learn

Download the Detailed Syllabus

Elevate your career with Immersive Machine Learning bootcamp

Instructors, Mentors & Career Coach

Mayuresh Shilotri

Mayuresh Shilotri

Indian Institute of Technology, Madras
jay Trivedi

Jay Trivedi

Data Scientist | IIM Ahemdabad | IIT Roorkee
Deepak Angrula

Deepak Angrula

Indian Institute of Technology, Bombay
Bhumil Haria

Bhumil Haria

Senior Software Engineer at Paycraft
Sudhanshu Saxena

Sudhanshu Saxena

Big Data - Hadoop Trainer, Data Scientist, Big Data Speaker
Mehul Chopra

Mehul Chopra

Fr conceicao rodrigues college of engineering
Rohit Ghosh

Rohit Ghosh

Indian Institute of Technology, Bombay
Soumendra Dhanee

Soumendra Dhanee

Institute of Mathematics and Applications
Sidharth Ramachandran

Sidharth Ramachandran

Indian Institute of Management, Kozhikode
Divyesh Shah

Divyesh Shah

Engineering Manager - Marketplace Management at Uber
Paul Meinshausen

Paul Meinshausen

Data Scientist @ Montane Ventures
Shweta Doshi

Shweta Doshi

Co-Founder @ GreyAtom - Head - Strategic Partnerships

“We also bring in a lot of Hiring Challenges and conduct Hackathons from our Industry partners to the classroom”

Our prime objective to provide our students with hands on experience in solving data science problems. For this we need a set of world class instructors having a perfect blend of both industry as well as academic experience. And hence we have built a pool of instructors varying from academicians from IIT to Data Scientists at companies like Haptik, Uber etc. We also bring in a lot of Hiring Challenges and Hackathons from our Industry partners to the classroom which develop various of skill set in our students.

Apart from that we have a dedicated team for Learning and Development which will help you with Job search and career opportunities even after the program. Our instructional staff conducts mock interviews, training exercises and role-play sessions designed to help you tackle the job interview.

"'Great learning experience' The real office simulation, daily standups, real tools and workflows that expert programmers employ excited me a lot. We were asked to build and deploy real products in our Capstone project led by Mentors from the industry. Not only that, we collaborated with other students on projects - simulating real-time teamwork. Moreover they conduct meetups, events webinars to bring us up to speed on relevant issues and building our professional network with other data practitioners who not only expanded knowledge but also gave us the chance to look beyond forcing us to be better and constantly motivated us to get us through the hard phase of learning by doing real work."

Nikhil Prasad
Winner of Startupbootcamp Fintech’s Hackathon (1st Prize) Mumbai

Industry Partners

"'Best Full Stack Course for Data Science' The instructors were amazing and the active involvement of founders was helpful. What I loved the most were guest speaker sessions. We got a great insight into what the industry needs which helped us learn the right skills."

Rishabh Mishra
Immersive Data Science Engineering Bootcamp, GreyAtom data science enthusiast

Career Services

Our experienced career services team will help you optimize and amplify your professional presence and help you draw more eyeballs.

Resume Building

Optimize your resume to match job filters for data science positions.

LinkedIn Presence

Learn clever hacks to improve visibility of your LinkedIn profile and get noticed by employers.

Interview Videos

Find out how to answer critical interview questions that make or break the deal.

Student Life

6 hours in-Campus

40% - Instructor-led lessons.
60% - Coding exercises.

Placement Assistance

Career Services

Learn some of the clever hacks to bump your resume and social profile to the top of the pile.

Recreation

Enjoy tea or coffee, jenga, and foosball!

Session with Industry Experts

Join your cohort in the evening for a talk by industry speakers.

Our Tool - Commit.Live

Commit.Live™ is our in-house, cloud-based training platform. Integrated with GitHub and Medium blogs, it brings the best of all worlds at the click of a button. With on-board performance analytics and gamification, Commit.Live offers a seamless, immersive learning experience.


Learn by Doing

Learn data science by doing actual work on industry data sets. Commit.Live combines Immersive Learning with real data sets to deliver optimum learning outcomes.

Integration with GitHub

Commit.Live allows users to auto-post their work on GitHub. Over time, it builds a portfolio of demonstrable skills that can be leveraged during job applications.

Assistance

Users can collaborate with peers on Commit.Live to troubleshoot problems. This leverages one of the key tenets of Immersive Learning - peer-to-peer collaboration.

Multifunctionality

Commit.Live is a custom variant of GitHub’s Atom text editor, combined with a fully-configured, virtualized terminal. Its integration with Jupyter Notebook allows you to code as you learn.

Real time dashboard

Commit.Live’s embedded analytics reflects the most current information about the user’s performance and assignment scores.

Build storytelling skills

A data scientist should be able to weave a compelling narrative around his work product. Commit.Live’s gamified blogging experience incentivizes users to blog on esteemed portals like Medium.com.

Admission

Batch

9th Jun

Sat, Sun • 9.30am-4.30pm
Mumbai
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Eligibility

To apply for the Immersive Machine Learning Bootcamp program, you must have

  • A Bachelor’s / Master’s degree (B.E., BCA, MCA, B.Sc, M.Sc, Ph.D.) in a discipline with a strong quantitative component like Mathematics / Physics / Statistics / Economics
  • Applicants with coding experience will be preferred

Minimum eligibility criteria may be waived for exceptionally qualified candidates.

Admission

How to Apply?

Selection Process

Common Process

  • Upon Review (ensuring your fitment to the Program), you will receive an invitation for personal interview.
  • Personal Interview with a panel comprising of Data Scientist and Full Stack Engineers will assess the candidate.
  • Once basic competence in engineering discipline is assessed, learnability, attitude are tested.

Fee Structure

Program Fee

₹85,000

  • Flexible EMI options
  • Discounts on full payment

Program Certificate

Users will be awarded a certificate upon the completion of the program. Also, users will enjoy lifetime access to learning materials, assignments, and quizzes.

Starts In

Immersive Machine Learning Bootcamp

Weekend - 13 weeks (Classroom) | Sat, Sun • 9.30am-4.30pm
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