Home MOOCs-List 5 Best NPTEL Machine Learning Courses

5 Best NPTEL Machine Learning Courses

NPTEL Machine Learning Courses

This article contains a list of top 5 machine learning online courses, MOOCs, classes and specialization for the year 2020 by NPTEL. The courses available are suitable for any type of learner be it a beginner, intermediate or a professional. So check out this list and find the most suitable NPTEL machine learning course for yourself. Follow here to get details about NPTEL Registration 2020 in various disciplines.

National Programme on Technology Enhanced Learning is a project that is being handled by the seven Indian Institutes of Technology. The program is to be used as a core curriculum content for training purpose and aims to provide a variety of courses in the field of Engineering and Core Sciences. Also, they have introduced a variety of courses in different disciplines that are suitable for all types of learners to take.

NoteCoursera is providing Free + Best Machine Learning Courses from top universities and organizations including Stanford University, Duke University, DeepLearning.ai, University of Washington, Imperial College of London, IBM, Google Cloud Training, John Hopkins University and more. Click Here

NPTEL Machine Learning Courses:

NPTEL along with providing a variety of online courses, certifications, specialization and MOOCs in different fields has also managed to introduce NPTEL Machine Learning courses. Those who are interested in learning the fundamentals, the logic, it’s working in short everything about machine learning are welcome to join in any of the courses.

ADMachine Learning

      • Stanford University via Coursera
      • 64 hours of effort required
      • 2,975,908+ students enrolled
      • ★★★★★ (129,908 Ratings)

saveIntroduction to Machine Learning

      • Prof. Sudeshna Sarkar via NPTEL
      • Co-ordinated by: IIT Kharagpur
      • 8 Weeks duration
      • Discipline: Computer Science and Engineering

This course covers extensive knowledge of Machine Learning. Sudeshna Sarkar, the Head of the Department of Computer Science and Engineering at IIT Kharagpur, has carefully formatted the syllabus of this course. A major focus of this course will be fundamental concepts in Machine Learning and popular machine learning algorithms. It also covers the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbour etc. By the end of this course, you will have in-depth knowledge about Bayesian learning and the naïve Bayes algorithm, support vector machines, basic clustering algorithms and a lot more.

saveMachine Learning

      • Prof. Carl Gustaf Jansson via NPTEL
      • Co-ordinated by: IIT Madras
      • 8 Weeks duration
      • Discipline: Computer Science and Engineering

Want to learn about Artificial Intelligence (AI) and how it is affecting our industries? Then this course is perfect for you. Prof. Carl Gustaf teaches how the scientific discipline of Machine Learning is focusing on the development of an algorithm for finding patterns or making predictions from empirical data. Know how Artificial Intelligence is frequently utilized by many professions and industries to optimize processes and implement adaptive systems. This course will also help you in acquiring techniques such as decision tree-based inductive learning, inductive logic programming, and reinforcement learning etc.

saveIntroduction to Machine Learning (Course sponsored by Aricent)

      • Dr. Balaraman Ravindran via NPTEL
      • Co-ordinated by: IIT Madras
      • Self-Paced
      • Discipline: Computer Science and Engineering

This course would be valued by any company in the data analytics, data science and big data domain. Learn the basic concepts of machine learning along with the various data-driven disciplines such as analytics. Furthermore, in this course, Professor Balaraman Ravindran will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms. At the end of this course, you will have comprehensive knowledge about linear regression, multivariate regression, early models, perceptron learning, partitional clustering, hierarchical clustering and a lot more. Enrol yourself now and learn from a mathematically well-motivated perspective.
Online Specializations

savePractical Machine Learning with Tensorflow

      • Prof. Ashish Tendulkar via NPTEL
      • Co-ordinated by: IIT Madras
      • 8 Weeks Duration
      • Discipline: Computer Science and Engineering

For those of you who want to learn about Machine Learning using TensorFlow, then this course might grasp your attention. With this course, you can cover the basics of TensorFlow and Machine Learning. This courses’ syllabus has been formulated by Prof. Balaraman Ravindran, an associate professor in Computer Science at IIT Madras and Mr Ashish Tendulkar, an experienced AI/ML professional. Get an overview of machine learning, data input, preprocessing with TensorFlow and machine learning model building. Moreover, with this course, you’ll gain information about advance TensorFlow, hardware accelerators, monitoring and evaluating models using Tensor board etc. After this course, you will be able to build ML models using TensorFlow.

saveMachine Learning for Engineering and Science Applications

      • Prof. Ganapathy & Prof. Balaji Srinivasan via NPTEL
      • Co-ordinated by: IIT Madras
      • 12 Weeks Duration
      • Discipline: Computer Science and Engineering

Through this course learn about Functional Programming. In this course, you can study about Haskell and how its programming features are being used for both building rapid prototypes and actual deployment. Prof. Balaji Srinivasan and Prof. Ganapathy Krishnamurthi will help you in understanding linear algebra, probability, numerical computation and optimization etc. Plus, learn about linear and logistic regression, neural networks, convolutional neural networks and recurrent neural networks. In the end, you’ll have all of the classical techniques regarding Bayesian regression, binary trees, k-Means, KNN, GMM etc. Similarly, you’ll have all of the advanced techniques regarding structured probabilistic models, Monte Carlo methods, autoencoders and generative adversarial networks.

ADDeep Learning Specialization

      • deeplearning.ai via Coursera
      • 96 hours of effort required
      • 305,958+ students enrolled
      • ★★★★★ (202,442 Ratings)

ADIntroduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

ADIBM AI Engineering Professional Certificate

      • IBM via Coursera
      • 128 hours of effort required
      • 11,497+ students enrolled
      • ★★★★☆ (8,314 Ratings)

So take any of the course now and never stop learning.

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