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How To Kickstart Your Machine Learning Journey: 101 Machine Learning Guides Here
- Linear Algebra tutorials by Kardi Teknomo - This interactive tutorial is a gem of a resource. Dr. Kardi Teknomo of Ateneo de Manila University explains every concept in a simple, easy to read the language.
- Linear Algebra in Twenty Five Lectures - A concise course by the University of California, Davis scholars Tom Denton and Andrew Waldron.
- Paul's Online Notes - A complete online resource (free to download) for math by Paul Dawkins of Lamar University. This resource is mainly centered around algebra and calculus.
- Stat Trek - Online website for statistics.
- Khan Academy - An all-time favorite among students, you would find a plethora of content on various areas of mathematics and statistics.
- Pythonprogamming.net - One of the best online resources for learning Python out there. The programmer behind this website, Harrison Kinsley (popularly known as Sentdex in the Python community) explains every aspect of Python perfectly!
- Automate The Boring Stuff by Al Sweigart - Another very good online resource on Python. Programming is deconstructed right from scratch. In fact, the beauty lies in how simple tasks can be automated through Python.
- ListenData for R - This website offers R tutorials for free. Extensive coverage of R concepts is what makes this site a catch for ML beginners preferring R over Python.
- Code Project - A popular discussion forum exclusively for discussing programming queries in general. With a big developer user base, ML beginners can share their code, ask where they face problems in the code and work around ideas.
- Machine Learning Mastery by Jason Brownlee - An amazing blog by expert Jason Brownlee. He explores the fascinating world of ML and captures its essence in the real world.
- Adam Geitey's blog- interesting write-ups in ML and Python
- Arthur Juliani's blog on Reinforcement Learning - an absolute gem of a blog which particularly focuses on reinforcement learning in ML.
- Edwin Chen's blog - it explores requisite concepts of ML such as neural networks, deep learning etc. as well as the math behind it.