Practical Machine Learning with Tensorflow

By Prof. Ashish Tendulkar, Prof. Balaraman Ravindran | Google, IIT Madras

Learners enrolled: 12193

This will be an applied Machine Learning Course jointly offered by Google and IIT Madras. We will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part. After this course, the students will be able to build ML models using Tensorflow. INTENDED AUDIENCE: Any Interested Candidates PREREQUISITES: Programming in Python, Data Mining or Machine Learning or Data Science

Summary

Course Status :	Completed
Course Type :	Elective
Language for course content :	English
Duration :	8 weeks
Category :	Computer Science and Engineering Artificial Intelligence Data Science
Credit Points :	2
Level :	Undergraduate
Start Date :	14 Sep 2020
End Date :	06 Nov 2020
Enrollment Ends :	25 Sep 2020
Exam Date :	20 Dec 2020 IST

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1: Getting started with Tensorflow

Week 2: Overview of Machine Learning (Process and Techniques, Demonstration of ML concepts with Deep Playground)

Week 3: Data Input and Preprocessing with Tensorflow

Week 4: Machine Learning Model Building

Week 5: Prediction with Tensorflow

Week 6: Monitoring and evaluating models using Tensorboard

Week 7: Advance Tensorflow (Building custom models - CNNs, Scaling up for large datasets)

Week 8: Distributed training with hardware accelerators

Books and references

www.tensorflow.org

Instructor bio

Prof. Ashish Tendulkar

Google

Dr. Ashish Tendulkar is a ML architect with Google Pay. He got his Master's and PhD from IIT Bombay and post-doc from Spain. He has held various positions including Assistant Professor at IIT Madras, VP of Data Science at Reliance and Principal Data Scientist at Media.net before joining Google.

Prof. Balaraman Ravindran

IIT Madras

Prof. Balaraman Ravindran is currently an Professor in Computer Science at IIT Madras and Mindtree Faculty Fellow . He has nearly two decades of research experience in machine learning and specifically reinforcement learning. Currently his research interests are centered on learning from and through interactions and span the areas of data mining, social network analysis, and reinforcement learning.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 20 December 2020 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 6 assignments out of the total 8 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Madras .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team