IIT Roorkee Post Graduate Certificate Program in Data Science & Machine Learning

IIT Roorkee Post Graduate Certificate Program in Data Science & Machine Learning

Programme Start Date: 26th March, 2022 | Admission: Close

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DELIVERY TYPE

Live online instructor-led sessions (Direct-to-Device)

COURSE DURATION

11 months

DEADLINE TO APPLY

20th March, 2022

COURSE FEE

₹ 300,000/- + Taxes =
₹ 3,54,000/-

CLASS TIMINGS

Saturday & Sunday –
4:00 p.m. to 6:00 p.m.

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1st

Among the IITs in the ‘Citations per Faculty’ parameter in QS World University Rankings, 2021

1-20

Among universities worldwide in ‘Citations per Faculty’ parameter in QS World University Rankings, 2021

83rd

In Asian University Rankings from Times Higher Education World University Rankings, 2020

7th

In overall rankings by NIRF, 2020

6th

Among Engineering colleges in NIRF, 2020

1st

In India Today’s list of India's Best Architecture Colleges 2020 (for Department of Architecture and Planning at IIT Roorkee)

Programme Highlights

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PG Certificate from IIT Roorkee

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3 specialisations to choose from - Computer Vision & Image Recognition, Speech Recognition, Data Engineering

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Live online teaching by IIT faculty, industry experts

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360-degree career support

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Career Support

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Industry expert speak

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Eligibility

  • Bachelor's Degree in Science/Engineering with minimum 50% score
  • No coding and mathematics background needed
  • Minimum 2 years of work experience

Programme Duration

Programme Delivery

  • The programme will be delivered in a two-way video/audio interactive mode.
  • Sessions will be delivered from IIT Roorkee studio on BCCL technology platform.
  • Students will attend the lectures on their own devices (Laptop / Desktop / Tablet / Mobile).
  • Students will have access to a Learning Management System for referring to the content.
  • Live online instructor-led sessions by top IIT faculty and industry experts.

Programme Roadmap

    Topics Covered

  • Emerging Technologies and AI
  • Understanding Data Science and AI

Learning Outcome
In this module, you will learn about foundations of Data Science. It is important that you are aware about the tools and technologies that are used for handling data.

    Topics Covered

  • Getting Started with Python
  • Data Structures in python, loops and control structures
  • Functional programming in python, creating UDF’s
  • Linear Algebra with NumPy and SciPy
  • Data Pre-Processing using Pandas
  • Generating plots with matplotlib

Learning Outcome
Get started with Python Programming for Data Science. You will be learning about the basics of Python and get introduced to popular Data Science libraries like NumPy, Pandas, and Matplotlib.

    Topics Covered

  • Descriptive Statistics
  • Foundations of Probability
  • Probability Distributions
  • Inferential Statistics
  • ANOVA and hypothesis testing

Learning Outcome
In this module, you will learn about different statistical techniques which can be used to summarise the data. Also, you will learn about generating hypothesis from the data and testing the same.

    Topics Covered

  • Sourcing Data from Different Sources 
  • Data Wrangling 
  • Cleaning Date and Text Columns
  • Exploratory Data Analysis
  • Working with SQL
  • Data Visualization using Tableau

Learning Outcome
Exploring the data and understanding that it is a really important step even before we apply any Machine Learning models. Visualising the data helps to share the insight with all the stakeholders and helps in getting greater insights. You will be using Tableau for visualising the data.

    Topics Covered

  • Linear Transformations and Eigen pairs in Machine Learning Singular value decomposition and Regularization of linear systems
  • PCA and Discriminant analysis
  • Gradient calculus and Numerical Optimization

Learning Outcome
In this module, you will learn the essential concepts of mathematics which will help you grasp the working of algorithms as you proceed in the programme.

    Topics Covered

  • Python ML Library - Scikit Learn
  • Introduction to ML- Types of Learning
  • Linear Regression 
  • Logistic Regression
  • k Nearest Neighbors 
  • Unsupervised Learning : Clustering & Dimensionality Reduction
  • Decision Trees
  • Support Vector Machines
  • Recommender System
  • Hands-on Case Studies for ML

Learning Outcome
In this module, you will learn about different Machine Learning algorithms and their applications. You will get hands-on practice with your very first predictions using Machine Learning models.

    Topics Covered

  • Text Analytics Overview
  • Sentiment Analysis on Text Data 
  • Naïve-Bayes Model for Sentiment Classification
  • Document Summarisation 
  • Topic Modelling 
  • Hands-on Practice

Learning Outcome
Learn to work with the unstructured text data. Learn about how to extract meaningful insights from text data and prepare the data for Machine Learning models.

    Topics Covered

  • Hyper Parameter Tuning
  • Overfitting and Regularisation
  • Ensemble Models
  • Gradient Descent and Stochastic Gradient Descent Algorithms
  • Gradient Boosting Machines 
  • Feature Engineering & Feature Selection Techniques
  • Time Series Forecasting

Learning Outcome
Optimising the Machine Learning models is one of the key steps. You will learn about how to tune the hyper parameters of a model and get the desired outcome for a particular business problem.

    Topics Covered

  • TensorFlow Overview
  • TensorFlow 1.X Programming Model
  • Tensors
  • Computational Graphs
  • Sessions
  • Linear Algebra with TensorFlow
  • TensorFlow 2.0
  • Hands-on Exercises with TF 1.x & TF2.0

Learning Outcome
TensorFlow is an end-to-end open source platform for Machine Learning. It provides advantages like easy model building, robust ML production and powerful experimentation. In this module, you will get hands-on experience of programming with TensorFlow.

    Topics Covered

  • Introduction to Perceptron
  • Perceptron Training
  • Deep Neural Networks
  • Keras API 

Learning Outcome
Get introduced to the concept of a neuron and how multiple neurons can be used to construct an Artificial Neural Network. Deep Learning is a class of Machine Learning algorithms that progressively extract features for better understanding of the problem. You will learn about various Deep Learning models built using Artificial Neural Networks.

    Topics Covered

  • Computer Vision with Open CV
  • Convolutional Neural Networks (CNN)
  • Pretrained CNN Models
  • Image Classification with KERAS
  • Object Detection
  • Hands-on Practice

Learning Outcome
One of the popular applications of Deep Learning is in image recognition. You will learn how to build complex image recognition and object detection models and apply them to solve business use cases.

    Topics Covered

  • Overview of Speech Recognition and Basic APIs
  • Advanced NLP - using Word Embeddings
  • Word2Vec, GLOVE
  • Sequence Models to Audio Applications
  • Recurrent Neural Networks – RNN
  • RNN for Sequence Modelling 
  • Time Series Forecasting with RNN

Learning Outcome
Processing the naturally spoken language is one of the complex tasks faced by researchers. In this module, you will learn about Natural Language Processing and how Deep Learning models can be used to build speech recognition applications.

    Topics Covered

  • Introduction to Data Engineering & Big Data
  • Introduction to Hadoop, HDFS and map reduce
  • Data Analytics using Apache hive
  • Working with Cloud(Microsoft Azure)
  • Working with Apache Spark and Spark through databricks
  • Data /ingestion tools – Sqoop, Flume and Kafka
  • Working with NoSQL databases – Cassandra, Hbase and MongoDB
  • Introduction to Big data analytics with Spark ML
  • Implementing ML algorithms through spark ML

Learning Outcome
Building the data pipelines and deploying the Machine Learning models are some of the important steps in implementing the DS and ML solutions in production. This module will help you learn these tools and techniques.

    Capstone Project Options

  1. Creating a Hierarchical Classification Tool for COVID-19 Literature
  2. Patch Classification from Image Labels – Healthcare
  3. Fraud Analysis Using ML Algorithms
  4. Enabling Business Intelligence for a Restaurant Aggregator
  5. Land Use classification based on Satellite Imagery
  6. Retail Customer Segmentation
  7. Telecom Customer Churn
  8. Data Quality and Data Exploration of Credit Card Dataset
  9. Chatbot using DialogFlow
  10. Lung Cancer Detection
  11. Bone Age using X-Ray Images
  12. Bank Loan Portfolio Data Pre-processing
  13. Taxi Trip Data Analysis
  14. COVID-19 Data Analysis
  15. COVID Vaccination Helpline- Customised Chatbot using Alexa Blueprint
  16. Prediction of COVID-19 spread using Time Series with LSTM

Learning Outcome
End-to-end project to apply the concepts taught in the program.

    Topics Covered

  • ML Model lifecycle
  • Flask API Deployment
  • Fast API Deployment
  • Non-API deployment
  • Deploying models on AWS & Azure
  • PySpark API

Learning Outcome
In this module you will learn about deploying the machine learning / deep learning modules using APIs on cloud.

    Mini Project and Case Studies

  1. IPL Analytics
  2. eBay Car Sales Analytics
  3. E-commerce Customer Shopping Analytics
  4. Classification of Human Activity Recognition
  5. Predictive Model to Forecast the Sales of Supermarket
  6. Customer Segmentation of Clickstream Online Retail Shopping Data
  7. Popularity Prediction of Social Media Articles
  8. Bitcoin Price Prediction
  9. Time Series Analysis of Energy Consumptions of Appliances
  10. Ensemble Techniques to Classify the Customer Churn
  11. Regularisation Techniques for IPL Auction Analysis
  12. Storytelling with Netflix Text Data
  13. Topic Modelling on Amazon Review Dataset
  14. Personality Classification based on MBTI Metric
  15. Fake News Detection
  16. Auto tagging of photos uploaded by the users on review website
  17. COVID-19 Detection Using Chest X Rays
11

Months course

450+

Hours

12

Modules

3

Specialisations

8

Tools

10+

Projects

10+

Case Studies

H

Hackathon

Compete to solve real business problems
Compete to solve real business problems

Faculty Profiles

Prof. Millie Pant

Associate Professor,

Department of Applied Science and Engineering,
IIT Roorkee

Prof. Millie Pant has been associated with IIT Roorkee since 2007. Her areas of interest include Numerical Optimisation, Operations Research and Supply Chain Management, among others.

Prof. Sumit Kumar Yadav

Assistant Professor,

Operations Management,
IIT Roorkee

Prof. Sumit Kumar Yadav completed his Ph.D. in Management from IIM Ahmedabad and B.Tech from IIT Bombay. His teaching experience spans IIT Roorkee and reputed management colleges.

Prof. Alok Bhardwaj

Assistant Professor,

Department of Civil Engineering,
IIT Roorkee

Dr Bhardwaj is a National Geographic Explorer and is supported by National Geographic Society to conduct research on natural hazards in Asia. Dr Bhardwaj’s main research interests include application of remote sensing, deep learning techniques, and digital image processing to study natural hazards.

Advisory Board

The PGCP-DSML advisory board comprises eminent industry experts and highly experienced academicians, guiding the overall content and approach of the programme. Their collective experience and knowledge lends unmatched rigour to the PGCP-DSML programme. The board plays an influential role in ensuring that the programme provides comprehensive, contemporary and industry-relevant education that enhances participants’ learning.

Dr. Sheela Siddappa
Mr. Hindol Basu
Mr. Joy Mustafi
Post Graduate Certificate Program in Data Science & Machine Learning

Post Graduate Certificate Program in Data Science & Machine Learning

Certificate of completion from IIT Roorkee.

certificate

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About IIT Roorkee

About IIT Roorkee

About TSW

About TSW

Programme Fees

₹ 300,000/- + Taxes = ₹ 3,54,000/-

Note: "In case the candidate rejects the offer from the Institute then the processing fees will be not refunded."

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