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Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
The Machine Learning for Engineering and Science Applications certification course offers a duration is 12 weeks which focuses on providing a a broad overview of modern algorithms in ML for engineers. This course covers the key aspects of machine learning such as the basics of ML, Linear Algebra, Numerical Computation, Neural, and Autoencoders.
Students gain hands-on training from industry experts in applying ML algorithms in the engineering field. The Machine Learning for Engineering and Science Applications certification by NPTEL intended audience is postgraduate students in all engineering and science fields and mature senior undergraduate students can also pursue this course.
The Highlights
- 12 Weeks Course
- Completion Certification
- For PG/UG Students
Programme Offerings
- videos
- assignments
- Hands-on Learning
- Books
- Transcripts
- Live Session
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIT Madras (IITM) |
Eligibility Criteria
Academic Qualifications
Postgraduation or mature senior undergraduate Students are eligible for the Machine Learning for Engineering and Science Applications certification course.
What you will learn
After completing the Machine Learning for Engineering and Science Applications certification syllabus, candidates will gain insights into leading industry cutting-edge techniques and principles. Students gain proficiency in utilising the learned methods and techniques to predict and develop models. They will also explore computational capabilities and understand the limits and application of the appropriate ML algorithms.
Upon completing the Machine Learning for Engineering and Science Applications training, the students will gain an in-depth understanding of modern algorithms in ML and their applications. The students gain the essential skills and knowledge required to implement the methods in open-source packages such as TensorFlow.
Who it is for
The Machine Learning for Engineering and Science Applications online course is designed for aspiring engineers or engineers working in the industry. This course will help the students and professionals to enhance their skills and knowledge. This course can also be beneficial for:
Admission Details
To join the Multimodal Interaction classes, follow these steps:
Step 1: Go to the link below:
https://nptel.ac.in/courses/106106198
Step 2: Candidates must “Login In” with the “Log In button” provided on the top right side of the page and create a separate account or “Log In with Microsoft or Google Account.”
Step 3: After filling out the details in the form submit and join the course.
The Syllabus
- Mathematical Basics 1 – Introduction to Machine Learning
- Linear Algebra
- Mathematical Basics 2 -- Probability
- Computational Basics – Numerical computation and optimization
- Introduction to Machine Learning packages
- Linear and Logistic Regression – Bias/Variance Tradeoff
- Regularization
- Variants of Gradient Descent
- MLE
- MAP
- Applications
- Neural Networks – Multilayer Perceptron
- Backpropagation
- Applications
- Convolutional Neural Networks 1 – CNN Operations
- CNN architectures
- Convolutional Neural Networks 2 – Training
- Transfer Learning, Applications
- Recurrent Neural Networks RNN
- LSTM
- GRU
- Applications
- Classical Techniques 1 – Bayesian Regression
- Binary Trees, Random Forests
- SVM
- Naïve Bayes
- Applications
- Classical Techniques 2 – k-Means
- kNN, GMM
- Expectation Maximization
- Applications
- Advanced Techniques 1 – Structured Probabilistic Models
- Monte Carlo Methods
- Advanced Techniques 2 – Autoencoders
- Generative Adversarial Networks
Evaluation process
Students are required to register themselves to appear in the examination. An optional proctored certification exam must be taken by students at a nominal fee at the end of the certificate course to receive the certificates from the IITs.