During these months of social distancing and lockdown, a lot of college students are not able to attend classes or go for internships where they could be learning practical tech skills. We, a team of volunteers comprising professors, industry professionals, and students working in data science, machine learning, and deep learning have gotten together to offer a course to students of engineering colleges on these topics. There is no fee for this course, and this is our way of contributing to the Indian and world community in these trying times.
We would like the course to be full of rewarding practical knowledge for the diligent students, which we will achieve by giving rigorous programming exercises and mini-projects to apply the concepts learned during the course. We will attempt to make sure that all diligent students who have the requisite background knowledge are able to make progress on the course by getting their doubts cleared in interactive video calls. Please see more details in the section about Topics and Pedagogy. The course is going to be rigorous but fun and rewarding to the diligent students who finish the entire course. However, between consecutive topics, we would allow any student to drop the course if one no longer wishes to continue given any circumstances.
Data Science (DS): Getting started, Basic data understanding, Improving plots, Basic statistics.
Machine Learning (ML): Introduction to ML, Decision trees, Bayesian decision theory, Linear models, Kernelization, Feature selection and engineering, Dense and shallow neural networks, Advanced topics in neural networks, Clustering, Model Explainability.
Deep Learning (DL) for Vision: Introduction to CNNs, Advanced conv nets, Semantic segmentation, Object detection, Instance segmentation, Few-shot learning, Metric learning, Generative Adversarial Networks (GANs), Variational Autoencoder (VAE).
Deep Learning for Natural Language Processing (NLP): Word embeddings, Language modeling, Simple applications of LSTMs, Advanced applications of LSTMs - 1, Relationship extraction, Advanced applications of LSTMs - 2, Advanced applications of LSTMs - 3, Beyond simple word embeddings, Beyond LSTMs, Chat-bot making.
Miscellaneous: Graph conv nets for NLP, Knowledge graphs, Reinforcement Learning.
Practical Implementation of ML Models: Deployment - 1, Deployment - 2, Deployment - 3, Front-end and logistics, Making APIs, Winning Kaggle, Compute and bandwith considerations, When to use which framework.
The course will be divided in small topics, which will require one or two days of intense work each. There will be approximately three topics covered each week for approximately eight weeks. The following will be the components of each topic:
After a group of modules, project statements will be released.
Please note that there will be no formal certificate given to the students for completing the course assignments. The best projects and assignments will be mentioned on the course website for all to see. Students are encouraged to make their GitHub portfolios and point to the course webpage, which will help them immensely for recruitment.
For detailed schedule, click here
Note: Interactive sessions will start at 9pm Indian Standard Time (IST).
Classes will resume from May 25 with a new schedule. This week, please catch up with the recorded lectures, assignments, and the tutorials on TensorFlow as given below:
If you are all caught up with the material covered so far, then please try some ML problems such as those given below:
Computer and connectivity: 8GB+ RAM, 20GB of free disk space, 100kbps+ connectivity
Knowledge: This course is directed at engineering students. Others who know the following topics are also welcome: Linear algebra (vectors and matrix arithmetic, projection of vectors, singular value decomposition), calculus (differentiation, partial derivatives, double derivatives, chain rule of derivatives), programming (preferably python, loops, conditions, functions, classes, programming best practices such as modularity, commenting, and informative variable naming).
Status: Student with a valid college ID card. Not restricted to India.
Study Materials: Please go through the following before April 15:
Note: This course will be beneficial to those who can put in several hours of work daily for 2-3 months, because there will be several hours of self-study, interactive video sessions, and 2-3 programming assignments each week. Casual learners may feel overwhelmed. Need good internet connection and knowledge of vector and matrix operations, differential calculus, and intermediate-level programming.
Disclaimer: There will be no certificate, and no university is formally involved. Students are expected to build their confidence and GitHub portfolios on their own. This is a volunteer effort in community service during this lock-down.
