1200+
48%
100+
175+
How GreyAtom will transition your career
——— Skills for job success ———
Machine Learning and NLP curriculum put together by hiring partners and academics.
——— Industry mentors ———
Experts from the industry teach concepts and guide learner projects.
——— Relevant projects ———
Apply your tech skills to solve real business problems.
——— Placement services ———
Career coaches, workshops, counselling and more to help you find the job of your dreams.
How you will learn
Live online mentor sessions, group study sessions, and offline networking events to give you maximum touchpoints and flexibility. Sessions are available in Mumbai, Bengaluru, and Hyderabad. More cities coming soon.
55+
160+
10+
Learner success stories
We have lots of experience in transitioning learner careers to Data Science.
Sonal Chandra
Before:
Associate Consultant at Capgemini
After:
Data Science Executive at Nielsen
Rohin Sequeira
Before:
Application Development Analyst at Accenture
After:
Data Analytics at PNB
Kaushik Bokka
Before:
Open Source Contributor at Uber
After:
Machine Learning Engineer at Fynd
After placing 1200+ learners, we have data-driven insights on what the industry requires. Hear what our learners have to say about their experiences with GreyAtom.
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Learner success video
Curriculum
Module 1
Data Science toolkit
- Fundamentals of Python
- Introduction to Git
Module 2
Python Modules
- Manipulating Data with Numpy
- Data Wrangling using Pandas
- Data Visualization with Matplotlib
Module 3
Statistical Tools for Data Science
- Summarizing Data with Statistics
- Introduction to Probability
- Making inference from Data
Module 4
Foundations of Machine Learning
- Make your first prediction with Linear Regression
- Optimizing linear regression models through Regularization
Module 5
Supervised Machine Learning
- Solving Classification problem using Logistic Regression
- Building the right Decision Trees
Module 6
Data Preparation for Machine Learning
- Exploratory Data Analysis
- Data Pre-processing Techniques
- Feature Selection Techniques
Module 7
Practical Machine Learning
- Ensembling Techniques and Random Forest
- Gradient Boosting Machines
- Unsupervised Machine Learning - Clustering
Module 8
Machine Learning in Production
- Challenges in ML
- Deployment of ML model
- From Business Problem to Data Science Problem
Module 9
Natural Language Processing
- Introduction to Natural Language Processing
- Analysing Text Data using Sentiment Analysis
- Using RASA tech stack to develop Chatbot
Module 10
Methods of Text Analytics
- Topic Modeling
- Language Models
- Parsing through Text Data
Module 11
Deep Learning & Neural Networks
- Multi Layer Perceptron
- Deep Neural Nets
- Optimizing Neural Nets
Capstones
Projects & Capstones
- Solving business problems using Data Science
Industry-relevant projects
The best way to learn Machine Learning is to practise concepts on business problems through projects. You can expect to tackle the projects below among others.
Fashion Fit Prediction
Use data from a leading online fashion marketplace to suggest the right clothing fit for customers.Car Insurance Claim
Help a car insurance major understand and predict why insurance claims were made or not made.Customer Intent Classification
Direct the customer queries to the right vertical by performing classification on customer queries.Built and mentored by industry experts, our emerging tech programs have been created to give you a solid foundation for high-growth careers.
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Watch video
Career services to ensure your successful outcome
—— 5 career counselling sessions ——
Practical advice from dedicated career coaches to help you reach your successful outcome.
——— 1 mock interview ———
Learn critical interview skills in order to showcase your knowledge.
——— Workshops———
Primers on how to fit into the workplace with important soft skills, and how to impress recruiters.
—— Portfolio and resume building ——
Create a digital presence and personal brand that shows off your ML and NLP skills to the industry.
Our alumni work at
Proof of your achievement
Each GreyAtom certificate comes with a unique and verifiable ID that you can share with employers, family and friends.
Admission process
This program is ideal for a tech professional with at least 1 year of work experience and basic coding knowledge, looking to become a Machine Learning Engineer.
Application
Fill out a short form to get started. We will create and track your admission, and will contact you using the information you provide.
Personal interview
A counsellor will call you to understand your goals, and to explain the program, its requirements and what it includes.
Seat confirmation
Accept the admission offer, and you are on your way to becoming a Data Scientist!
Program fees
Flexible payment options with EMI. Scholarships available for eligible candidates.
Internet banking
Credit/Debit cards
Cheque
EMI options available