This 29-part course consists of tutorials on ML concepts and algorithms, as well as end-to-end follow-along ML examples, quizzes, and hands-on projects.

Once done, you will have an excellent conceptual and practical understanding of machine learning and feel comfortable applying ML thinking and algorithms in your projects and work.

The primary objectives of this course are:

An updated, more cohesive version of this list is available here: Deep Learning, from novice to expert

Tutorials have been chosen to maximize learning curve, i.e. learn the most in the shortest amount of time. Tutorials cover topics from basic deep learning all the way to research done within the last 1 year! They cover significantly more material than a typical deep learning course and take lesser time.

Expected time to completion: 4-6 weeks (20-30 sessions)

Pre-requisites: Calculus (specifically, differentiation)

We'll cover the following topics:

• Machine Learning basics (what it is, gradient descend)
• Deep Learning basics (what it is, neural networks, computational graphs, back-propagation)
• Convolutional Neural Networks (among other...

Artificial Intelligence

• A Roadmap towards Machine Intelligence
• Building Machines That Learn and Think Like People
• What Question Would Turing Pose Today?

Techniques

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training
• Advances In Optimizing Recurrent Networks
• Recurrent Neural Network Regularization
• Dynamic Capacity Networks

Books

• DeepLearningBook.org by Ian Goodfellow, Yoshua Bengio and Aaron Courville
• NeuralNetworksandDeepLearning.com by Michael Nielsen

Courses

• Neural Networks for Machine Learning (2017) by Geoffrey Hinton, University of Toronto
• CS231n Convolutional Neural Networks for Visual Recognition by Andrej Karpathy, Stanford University
• Natural Language Processing with Deep Learning by Christopher Manning and Richard Socher, Stanford University
• Deep Natural Language Processing (2017) by Phil Blunsom, Oxford University and Google DeepMind
• Deep learning (2015) by Nando de Freitas, Oxford University
• Deep Learning for Self-Driving Cars (2017)...

Hi, I am Shagun Sodhani, a computer science graduate from Indian Institute of Technology (IIT), Roorkee. Presently, I am working with the Analytics and the Data Science team at Adobe Systems. In this role, I actively contribute to solving novel problems in the domain of Machine Learning and Natural Language Processing and developing valuable solutions for Adobe. Recently, I also won the Outstanding Young Engineers Award at Adobe Systems.

Along with my full-time commitments at Adobe, I have worked as a teaching assistant with Databricks for Data Science and Engineering with Apache® Spark™ MOOC series. The course was designed by faculty from UC Berkeley, UC Los Angeles and Databricks and was offered on the edX platform. I regularly attend tech-talks and meetups as well. I have delivered talks and workshops at events like PyCon India 2016 and Big Data Training Program, IIT Roorkee (organised by Dept. of Science and Technology, Govt. of India). Since August 2015, I have committed myself into reading and summarising one research paper every week which has helped me to develop a good understanding of Machine Learning and related domains.

Moderator note: We are very excited to have Shagun d...