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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

Candidates looking for an undergraduate or postgraduate course in machine learning should enroll in the Introduction to Machine Learning program. It is an elective twelve-week course in the field of computer science and engineering, programming, artificial intelligence, and data science. A collaboration of IIT Madras and NPTEL, this course is an initiative by the Ministry of Education, Government of India, and is available on the learning platform Swayam.

Introduction to Machine Learning certification will be taught by professor Balaraman Ravindran. The syllabus includes various topics from the data-driven disciplines such as machine learning and analytics. Candidates will be introduced to different learning paradigms and some popular algorithms and architectures used in these paradigms.

Introduction to Machine Learning is an online free audit course that offers an e-certificate for interested participants. Candidates will attend the online lectures, and submit assignments periodically. There is also an optional offline examination for participants who want to achieve certification.

The Highlights

Ministry of Education, Government of India initiative
The elective course of 12 weeks
IIT Madras educator
A free online audit course
Verifiable e-certificate available
Undergraduate/postgraduate course in computer science and engineering, programming, artificial intelligence, and data science
FDP course approved by AICTE
Assignments and optional offline exam
Books and reference for extra learning

Programme Offerings

Free online audit course
assignments
Optional offline exam
Undergraduate/Postgraduate Course
Computer Science and Engineering
Books And References
elective course
12 weeks duration
IIT Madras educator
Verifiable e-certificate available

Courses and Certificate Fees

Fees Informations	Certificate Availability	Certificate Providing Authority
INR 1000	yes	IIT Madras

Introduction to Machine Learning training MOOC is a free course. There is no enrollment fee. An exam fee of Rs 1,000 is applicable for participants who want to attempt the end-of-term examination.

Introduction to Machine Learning program fee structure

Course Name	Fee
Introduction to Machine Learning (Exam fee)	Rs. 1,000

Eligibility Criteria

Interested candidates are encouraged to study up the basics of programming, linear algebra, and probability theory before joining the Introduction to Machine Learning program.

Certificate Qualifying Details

Participants of the course will have to submit all the assignments, appear for the final exam and get the minimum passing score in assignments, final exam, and final score to get an e-certificate for the course.

What you will learn

Knowledge of AlgorithmsMachine learningData science knowledge

Introduction to Machine Learning program will cover the following topics:

Mathematically well-founded introduction to machine learning
Data analytics
Algorithms and architecture in different learning paradigms

Who it is for

Introduction to Machine Learning is designed for undergraduate and postgraduate engineering students, MS students, and doctoral candidates.

Admission Details

Candidates can enrol in Introduction to Machine Learning by Swayam by following this step-by-step guideline:

Go to the website https://onlinecourses.nptel.ac.in/noc21_cs24/preview on the Swayam learning platform and select the ‘Sign in/Register’ button on top of the page.
Enter your credentials and sign in. If you are using Swayam for the first time, create an account by choosing a username and password and enter a valid email address.
After the registration process is complete, fill up the online form and provide any documents that may be required.
Next, pay the exam fee if you want to attempt the exam.

Once the payment is complete, your enrollment is complete, and you can start learning on the start date.

Application Details

Introduction to Machine Learning program has an online form. But it can only be accessed after the registration or login process is complete. To complete the registration process, individuals need to enter a valid email address and choose an unused username and password. They can also register using their Microsoft, Facebook, or Google account.

The Syllabus

Linear algebra
Probability theory
Convex optimisation - (Recap)

Introduction: statistical decision theory

Regression
Bias Variance
Classification

Shrinkage methods
Principal component regression
Linear regression
Partial least squares
Multivariate regression
Subset selection

Linear classification
Linear discriminant analysis
Logistic regression

Perceptron
Support vector machines

Neural Networks

Introduction
Early Models
Perceptron Learning
Backpropagation
Initialization
Training & Validation

Parameter estimation

MLE
Bayesian estimation
MAP

Decision Trees
Regression Trees
Stopping Criterion & Pruning loss functions
Categorical Attributes
Multiway Splits
Missing Values
Decision Trees
- Instability Evaluation Measures

Bootstrapping & Cross Validation
Class Evaluation Measures
ROC curve
MDL
Ensemble Methods
- Bagging
- Committee Machines
- Stacking
- Boosting

Gradient Boosting
Random Forests
Multi-class Classification
Naive Bayes
Bayesian Networks

Undirected Graphical Models
HMM
Variable Elimination
Belief Propagation

Partitional Clustering
Hierarchical Clustering
Birch Algorithm
CURE Algorithm
Density-based Clustering

Gaussian Mixture Models
Expectation Maximization

Learning Theory
Introduction to Reinforcement Learning
Optional videos (RL framework, TD learning, Solution Methods, Applications)

Evaluation process

While Introduction to Machine Learning training has an online exam, it is not the only criteria to achieve certification. Participants need to submit all the assignments and get the minimum passing score in the final exam and assignments.

A final score will be calculated based on 25% of the assignment marks and 75% of the exam marks. The final passing score is 40/100. Only candidates who score 10/25 and above in assignments and 30/75 and above in the exam in addition to the passing score will be awarded the certificate.