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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom, Campus Based/Physical Classroom	Video and Text Based	Weekends

Course Overview

IIT Roorkee has developed the Post Graduate Certificate Program in DS & ML online course to provide candidates with live, instructor-led sessions on machine learning and data science. This is an 11-month long postgraduate programme where candidates will train under esteemed faculty from IIT Roorkee, interact with industry leaders for gaining one-on-one knowledge and get industry mentorship.

During the Post Graduate Certificate Program in DS & ML training, candidates will get to work with eight advanced tools like Python, Keras, TensorFlow, and more. The IIT faculty will teach the course via the interactive audio-visual mode and live sessions with the candidates. Throughout the 11 months, candidates will get over 450 hours of rigorous training and experience.

The Post Graduate Certificate Program in DS & Machine Learning classes by IIT Roorkee carries tremendous benefits for working professionals who want to accumulate knowledge in this field. Candidates will get the opportunity to work on multiple projects to build their portfolio.

The Highlights

IIT Roorkee offering
Postgraduate certification
Audio-visual training
Live interactions
IIT Roorkee faculty
11 months duration
Industry interaction
10+ projects
Weekend classes
Case studies
Option of three specialisations

Programme Offerings

11 months duration
Case Studies
Live interaction
IIT Roorkee faculty
Industry interaction and mentorship
450+ hours
Hackathon
Specialisations
Extensive curriculum
Certification

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Roorkee

The Post Graduate Certificate Program in Data Science & Machine Learning fee is Rs. 3,00,000 along with Rs. 54,000 as taxes.

Post Graduate Certificate Program in Data Science & Machine Learning Fee Structure

Description	Amount (Including GST)
Programme Fee	Rs. 3,54,000/-

Eligibility Criteria

To enroll in the Post Graduate Certificate in DS & ML course, candidates need to have an undergraduate degree in Engineering/Science with at least 50% and a minimum of two years’ work experience.

What you will learn

Statistical skillsKnowledge of AlgorithmsMachine learningData science knowledgeKnowledge of Python

After completing the Post Graduate Certificate in DS & ML programme, candidates will become adept in:

The foundation of data science
Mathematical concepts for understanding algorithms
Basics of Python programming
Statistical techniques used to summarise data
Generating and testing hypotheses
Various machine learning algorithms and their applications
Programming using TensorFlow

Admission Details

For admission to the Post Graduate Certificate Program in Data Science & Machine Learning classes, candidates may see the admission process below:

Step 1: Follow the course URL: https://timestsw.com/technology/courses/iit-roorkee-post-graduate-certificate-program-data-science-machine-learning/.

Step 2: Then the students may on the website, find, and click on the button ‘Apply Now’.

Step 3: Next, the students shall fill in an application form, and then put it through TSW.

Step 4: After the application form, an application fee should be offered for submission.

Step 5: As soon as the above details are confirmed after submission, an Online Application number will be generated.

Step 6: Only if the application has been accepted, the student gets an intimation.

Application Details

The application form has some fields where filling in details are necessary. The mandatory fields include the mobile number, name, academic details, mailing address, and the employment information of the participants.

The Syllabus

Emerging Technologies and AI
Understanding Data Science and AI

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

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

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

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

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

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

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

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

Introduction to Perceptron
Perceptron Training
Deep Neural Networks
Keras API

Computer Vision & Image Recognition

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

Speech Recognition

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

Data Engineering

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

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

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

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

Evaluation process

The certification is based on the successful evaluation of the students. The students will be asked to attend all the modules, quizzes, capstone projects, and assignments. They must pass in all these with a 60% score in the quizzes. The weightage being considered is 10% in the quizzes, 60& in the assignments and projects, and another 20% in the capstone project.