Machine Learning

Machine Learning datasets, modeling templates and learning resources.

Learning Resources

Python Learning Resources

https://forums.fast.ai/t/recommended-python-learning-resources/26888

Machine Learning and AI Courses

NOTE : Below listed resources are taken from Discord Server Artificial Intelligence (invite link: https://discord.gg/9TVtVEZ)

Here is a collection of some of my favorite FREE online courses on AI/ML:

1. The best and the most popular ML course is Andrew Ng's (Stanford) ML course: https://www.coursera.org/learn/machine-learning

2. If you want to learn deep learning (preferably after you learn ML), here are the two most popular courses: i) Andrew Ng's DL course (I recommend this one): https://www.coursera.org/specializations/deep-learning or www.deeplearning.ai. ii) The quick but possibly harder and possibly more practical courses: www.fast.ai

3. If you want to learn artificial intelligence (AI) in general, here's a course on AI taught by the guy who literally wrote the book on AI: https://classroom.udacity.com/courses/cs271

4. I recommend learning mathematics in college/university but if you don't have time or unable or need a review, i) here's a course: https://www.coursera.org/specializations/mathematics-machine-learning ii) Mathematics for computer science pdf: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/readings/MIT6_042JF10_notes.pdf iii) GRE math review (you should all know the math in this book, it's basically high school level but it's a very good review if you forgot some of it): https://www.ets.org/s/gre/pdf/gre_math_review.pdf

5. More challenging ML/AI courses, caution: Berkley's courses are graduate level: i) Berkley's ML course: https://see.stanford.edu/course/cs229 ii) Berkley's AI course: http://ai.berkeley.edu/home.html iii) Columbia's AI course: https://courses.edx.org/courses/course-v1:ColumbiaX+CSMM.101x+1T2018/course/

   iv) Geoffrey Hinton's Neural Networks course: https://www.coursera.org/learn/neural-networks/home/info v) Berkley's Deep Learning course (very good but very difficult): http://cs231n.stanford.edu/

6. More ML (data science) courses: i) Python ML course: https://www.coursera.org/specializations/data-science-python ii) TensorFlow crash course: https://developers.google.com/machine-learning/crash-course/ iii) Python data analysis course: https://www.coursera.org/learn/python-data-analysis

7. RL courses: ( Guide: https://karpathy.github.io/2016/05/31/rl/) i) Best RL lectures (somewhat easy but not as detailed as other RL courses) (By the guy who made AlphaGo): https://www.youtube.com/playlist?list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT ii) Berkley's RL course (difficult but very detailed): https://web.stanford.edu/class/cs234/schedule.html iii) Udacity's RL course: (slightly less difficult than Berkley's course, again, very detailed): https://classroom.udacity.com/courses/ud600 iv) Berkley's Deep RL course (heavy on math): http://rail.eecs.berkeley.edu/deeprlcourse/ v) John Schulman Deep Renforcement Learning YouTube lectures: https://www.youtube.com/watch?v=aUrX-rP_ss4

8. Stanford NLP course: http://web.stanford.edu/class/cs224n/syllabus.html

9. Other courses: i) MIT's course for self-driving cars (easy/fun course with lots of famous guest speakers) : https://www.youtube.com/watch?v=-6INDaLcuJY&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf ii) MIT's Artificial General Intelligence (AGI) aka True AI course (easy/fun course with lots of famous guest speakers) : https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4

10. If you're wondering which college/university major is right for A.I. or M.L., I think most experts will say that it is Computer Science. Getting a double major (or a minor) in mathematics would also be very beneficial. If you can't do this, I recommend taking a lot of extra mathematics courses as a computer science major. The most important math topics for ML are probably: Calculus, Statistics, Linear Algebra, and Discrete Structures/Algebra (perhaps also Mathematical Statistics, Abstract Algebra, Logic Theory, Set Theory, Probability Theory, Graph Theory, and so on). Also good to note that since most of A.I. today is done on computers, you need to make sure you become a good programmer, so I recommend studying topics like algorithms, computer languages, operating systems (or any other class where you can learn UNIX or Linux), and data structures. The basic mathematics and programming courses (Calculus and below) can be found on Khan Academy. The more advanced courses can be found on YouTube just by searching the course name and adding "MIT" or some other famous university name or from other individuals teaching the course and posting lectures on YouTube.

Contributions

Please add datasets in More Datasets folder by creating a new sub folder with name that of the dataset.