Courses: Online & Class-room

Udemy Courses by Lazy Programmer

Course order taken from this link of LazyProgrammer

1. Deep learning in Python - Part 1, 55 lectures, 6 hrs
2. Practical Deep Learning in Theano + Tensorflow - Part 2, 31 lectures, 4 hrs, Paid on 06-Jun-2017
3. Deep Learning - CNN in Python - Part 3, 30 lectures, 4 hrs
4. Unsupervised Autoencoders RBM - Part 4
   • Pre-requisite covers Cluster analysis
5. Deep Learning: RNN in Python - Part 5, 39 lectures, 5 hrs
6. NLP with Deep Learning in Python - Part 6, 45 lectures, 5 hrs, Paid on 06-Jun-2017
   • Pre-requisite covers NLP basics
7. Deep Reinforcement Learning - Part 7
   • Pre-requisite covers MDPs

Other Courses

1. Learning From Data: CaltechX CS1156x Course, by Prof.Yaser S. Abu-Mostafa
   Registered on 20th Sep for this & requires 10hrs per week, starting from 30th Sep, 2013.
   Created edX account with appanponn@gmail.com
2. Neural Networks for Machine Learning, Coursera, Oct 2012, by Geoffrey Hinton
3. Machine Learning by Andrew Ng, Coursera, Oct 2013
   Doing this course since it started. Need to see how much I can apply this knowledge to Kaggle problems.
4. Web Intelligence and Big Data, Coursera, Oct 2013, by Dr. Gautam Shroff
   Enrolled but not able to keep pace.
5. Statistics 202: Statistical Aspects of Data Mining, Summer 2013
6. Statistical Learning & Data Mining III, 20-21 Mar, 2014, 4th in the series, '''Paid Class-room Course''', by Hastie
7. CS142: Intro. to Machine Learning, Fall 2013, Brown Univ. Course.
   Nicely scheduled class with slides & homework assignments and also very recent !!
8. Discrete Inference and Learning in Artificial Vision, Jan 2014, Coursera Course
9. Probabilistic Graphical Models, by Daphne Koller,
   Youtube Playlist of 94 Video lectures, totaling 23 hours. Mar 2012.
10. Machine Learning & Probabilistic Graphical Models (CSE574), by Prof.Sargur Srihari.
    Slides are from Fall 2013 version but video are from old class & not all are available.
    Downloaded slides to:SONY8GB/online_courses/cse574/slides
11. Large Scale Machine Learning - CILVR in NYU, given by Langford (author of Vowpal Wabbit) & LeCun, Spring 2013.
    Source code Github repo