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...
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)
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...
TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.
This course introduces you to basics of TensorFlow — once done, you should feel comfortable around basic concepts like Checkpoints, Feature Columns, Datasets etc.