The first half is my learning path in the past two years while the second half is my plan for this year. Hope the list is helpful!
- Stanford Statistical Learning: Course page
- Coursera Stanford by Andrew Ng: Coursera, Youtube
- Stanford 229: Youtube, Course page
- Machine Learning Foundations (機器學習基石): Coursera , Youtube
- Machine Learning Techniques (機器學習技法): Youtube
- CMU 701 by Tom Mitchell: Course page
- Introduction to Statistical Learning: pdf
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science: pdf
- The Elements of Statistical Learning: pdf
Statistical Learning is the introduction course. It is free to earn a certificate. It follows Introduction to Statistical Learning book closely. Coursera Stanford by Andrew Ng is another introduction course course and quite popular. Taking either of them is enough for most of data science positions. People want to go deeper can take 229 or 701 and read ESL book.
- Stanford - Basic NLP course on Coursera: Videos, Slides
- Stanford - CS224n Natural Language Processing with Deep Learning: Course web, Videos
- CMU - Neural Nets for NLP 2017: Course web, Videos
- University of Oxford and DeepMind - Deep Learning for Natural Language Processing: 2016-2017: Course web, Videos and slides
- Sequence Models by Andrew Ng on Coursera: Coursera
- Speech and Language Processing (3rd ed. draft): Book
- An Introduction to Information Retrieval: pdf
- Deep Learning (Some chapters or sections): Book
- A Primer on Neural Network Models for Natural Language Processing: Paper. Goldberg also published a new book this year
- NLTK: http://www.nltk.org/
- Standord packages: https://nlp.stanford.edu/software/
The basic NLP course by Stanford is the fundamental one. SLP 3ed follows this course. After this, feel free to take one of the three NLP+DL courses. They basically cover same topics. The Stanford one have HWs available online. CMU one follows Goldberg's book. Deepmind one is much shorter.
- Ng’s deep learning courses: