Online Courses and Certifications

I believe in the importance of lifelong learning and hence keep try to upskill myself with technologies that I want to explore. Hence, I enroll myself in online courses related to that and have included some of the ones that I have been able to complete. Some of them are motivated by my desire to understand their use for my research, while others have been just out of curiosity.


  1. DeepLearning.AI - Sequences, Time Series and Prediction (link).

  2. DeepLearning.AI - TensorFlow Developer certificate (link).

  3. DeepLearning.AI - Natural Language Processing in TensorFlow (link).

  4. DeepLearning.AI - Convolutional Neural Networks in TensorFlow (link).

  5. Coursera's Fundamentals of Reinforcement Learning by University of Alberta (link).

  6. Coursera's Mathematics for Machine Learning: Multivariate Calculus by Imperial College London (link).

  7. Coursera's Probability and Statistics: To p or not to p? by the University of London (link).

  8. Coursera's Applied Machine Learning in Python by University of Michigan (link).

  9. Coursera's Introduction to the Internet of Things and Embedded Systems by the University of California, Irvine (link).

  10. Coursera's Digital Signal Processing by École Polytechnique Fédérale de Lausanne (link).

  11. Coursera's Learning How to Learn: Powerful mental tools to help you master tough subjects by Deep Teaching Solutions (link) highly recommended.

  12. Coursera's Wireless Communications for Everybody by Yonsei University (link).

  13. Coursera's Wireless Communication Emerging Technologies by Yonsei University (link).

  14. Coursera's Programming for Everybody (Getting Started with Python) by the University of Michigan (link).



My biggest regret is not having studied mathematics in a rigorous manner as most of the engineering maths classes seem to not provide much time to delve deep into them. Though I haven't been able to yet put the required time and effort into learning the maths involved in some of the complex machine learning techniques, I try to have a fair understanding of why a certain algorithm is likely to improve over time, given some assumptions on the distribution of the data. I am planning to work more deeply with some new techniques like Generative Adversarial Networks and Federated Learning to have a better understanding of the maths involved.