IIT Roorkee - Data Science and Machine Learning Course

Topics Covered

• Emerging Technologies on AI
• Understanding Data Science and AI
• Fundamentals of Programming
• Foundations of Statistics

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

• Emerging Technologies on AI
• Understanding Data Science and AI
• Fundamentals of Programming
• Foundations of Statistics

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
• Hands-on Linear Algebra with NumPy
• Hands-on Data Pre-Processing using Pandas
• Preparation of Times Stamp Intel Distribution of Python

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

Topics Covered

• Descriptive Statistics
• Foundations of Probability
• Probability Distributions
• Inferential Statistics

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 
• Working with SQL Primer
• Designing your Own Data for Business Problem
• Exploratory Data Analysis
• Data Visualisation 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

• 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: Text Analytics - Sentiment Analysis

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

• 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

• Computer Vision with Open CV
• Convolutional Neural Networks (CNN)
• Pretrained CNN Models
• Image Classification with KERAS
• Object Detection
• Hands-on Practice on Healthcare, Automobile and Retail Analytics

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.

Topics Covered

1. Creating a Hierarchical Classification Tool for COVID-19 Literature
2. Patch Classification from Image Labels – Healthcare
3. Traffic Sign Recognition

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