Live online instructor-led sessions (Direct-to-Device)
DEADLINE TO APPLY
20th March, 2022
₹ 300,000/- + Taxes =
Saturday & Sunday –
4:00 p.m. to 6:00 p.m.
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PG Certificate from IIT Roorkee
3 specialisations to choose from - Computer Vision & Image Recognition, Speech Recognition, Data Engineering
Live online teaching by IIT faculty, industry experts
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- Bachelor's Degree in Science/Engineering with minimum 50% score
- No coding and mathematics background needed
- Minimum 2 years of work experience
- 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.
- Emerging Technologies and AI
- Understanding Data Science and AI
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.
- 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
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.
- Descriptive Statistics
- Foundations of Probability
- Probability Distributions
- Inferential Statistics
- ANOVA and hypothesis testing
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.
- Sourcing Data from Different Sources
- Data Wrangling
- Cleaning Date and Text Columns
- Exploratory Data Analysis
- Working with SQL
- Data Visualization using Tableau
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.
- 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
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.
- 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
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.
- Text Analytics Overview
- Sentiment Analysis on Text Data
- Naïve-Bayes Model for Sentiment Classification
- Document Summarisation
- Topic Modelling
- Hands-on Practice
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.
- 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
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.
- TensorFlow Overview
- TensorFlow 1.X Programming Model
- Computational Graphs
- Linear Algebra with TensorFlow
- TensorFlow 2.0
- Hands-on Exercises with TF 1.x & TF2.0
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.
- Introduction to Perceptron
- Perceptron Training
- Deep Neural Networks
- Keras API
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.
- Computer Vision with Open CV
- Convolutional Neural Networks (CNN)
- Pretrained CNN Models
- Image Classification with KERAS
- Object Detection
- Hands-on Practice
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.
- 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
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.
- 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
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.
- 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
Capstone Project Options
End-to-end project to apply the concepts taught in the program.
- ML Model lifecycle
- Flask API Deployment
- Fast API Deployment
- Non-API deployment
- Deploying models on AWS & Azure
- PySpark API
In this module you will learn about deploying the machine learning / deep learning modules using APIs on cloud.
- 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
Mini Project and Case Studies
Prof. Millie Pant
Department of Applied Science and Engineering,
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
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
Department of Civil Engineering,
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.
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.
Post Graduate Certificate Program in Data Science & Machine Learning
Certificate of completion from IIT Roorkee.
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About IIT Roorkee
₹ 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."