A List of Data Science/Machine Learning Resources (Mostly Free)
1,132
48 commits
updated May 31, 2024
The first half is more or less my learning path in the past two years while the second half is my plan for this year. I tried to make a balance between comprehension and doability. For more extensive lists, you can check Github search or CS video lectures
Hope the list is helpful, especially to whom are not in CS major but interested in data science!
You may find the list overwhelming. Here is my suggestion if you want to have some basic understanding in one month:
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.
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.
Some other people's collections: NLP, DL-NLP, Speech and NLP, Speech, RNN
Ng's courses are already good enough. Reading Part 2 of Goodfellow's book can also be helpful. Learning one kind of DL packages is important, such as Keras, TF or Pytorch. People may choose a focus, either CV or NLP. People want to have deeper understanding of DL can take Hinton's course and read Part 3 of Goodfellow's book. Fast.ai has very practical courses.
45 followers · starred Feb 2018
85 followers · starred Jun 2018
20 followers · starred Mar 2018
22 followers · starred Jun 2023
A List of Data Science/Machine Learning Resources (Mostly Free)
1,132
48 commits
updated May 31, 2024
The first half is more or less my learning path in the past two years while the second half is my plan for this year. I tried to make a balance between comprehension and doability. For more extensive lists, you can check Github search or CS video lectures
Hope the list is helpful, especially to whom are not in CS major but interested in data science!
You may find the list overwhelming. Here is my suggestion if you want to have some basic understanding in one month:
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.
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.
Some other people's collections: NLP, DL-NLP, Speech and NLP, Speech, RNN
Ng's courses are already good enough. Reading Part 2 of Goodfellow's book can also be helpful. Learning one kind of DL packages is important, such as Keras, TF or Pytorch. People may choose a focus, either CV or NLP. People want to have deeper understanding of DL can take Hinton's course and read Part 3 of Goodfellow's book. Fast.ai has very practical courses.
45 followers · starred Feb 2018
85 followers · starred Jun 2018
20 followers · starred Mar 2018
22 followers · starred Jun 2023
