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. You can think of this course as a "Free Online Nano Book".

Once done, you will have an excellent conceptual and...

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...

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...

Hey commonloungers!!!

I am planning to start learning "machine learning" for kaggle competitions as I have a lot of free time for about 2 months.

My main motive is to participate in Kaggle, hackerearth competitions and ML hackathons. I have a very basic computer(32-bit architecture and just 2 gigs of RAM).

What should be my strategy to maximize my learning curve in these 2 months.

P.S.- Do comment about hardware requirements and at present, I don't have an iota of idea about machine learning