Mathematics For ML Is Difficult
Until you have these 8 free resources in hand…
👉🏽 Machine Learning ( and AI as a whole ) is a math-heavy field. It highly relies on concepts of Linear Algebra, Probability, Calculus and Statistics which might be difficult sometimes, to some developers. Learning these concepts and developing intuitive for them would help us in understanding the working for many ML algorithms and techniques.
👉🏽 To what depth you are learning these concepts, depends heavily on your interest. For researchers, these concepts must be known with full-depth whereas for developers there might be some basic requirements.
👉🏽 We have curated this list of 8 resources to learn mathematics for machine learning for free. To give yourself a stand, these free resources are helpful. If you think you can go ahead, there are a number of paid courses as well.
This will be a short story, so make you read it till the end!
✏️ 3Blue1Brown’s YouTube Channel — By Grant Sanderson
This could be one of the best YT channels to learn basics of linear algebra and probability. Intuitive examples with visualizations are key aspects of Grant Sanderson’s amazing videos.
🎹 Stat Quest's YouTube Channel — By Josh Starmer
StatQuest, by Josh Starmer, is one of my favorite YT channels to learn the Math of ML. Also, if you love music, these videos are just for you! Josh Starmer explains each concept with a concrete example and beautiful visualizations. You shouldn’t leave this one!
🧔🏽 Khan Academy
KhanAcademy was founded in 2006 by Salman Khan
KhanAcademy has been a platform providing high-quality education for free right from kindergartens to linear algebra, probability and calculus. Their videos are well-structured and also include exercises to test your skills. Their courses aren’t ML specific but are good start if you’re familiar with high-school mathematics.
🎓 MIT OpenCourseWare — Lectures by Gilbert Strang
MIT OpenCourseWare has really been a store house of lectures by professors at MIT ( USA ). Lectures on linear algebra, by Gilbert Strang, are an excellent resource to learn linear algebra intuitively. Gilbert Strang has been teaching at MIT for over 60 years and has published a bunch of textbook on linear algebra.
📚 “Mathematics for Machine Learning” — book by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
The book is available as a PDF for free.
“Mathematics for Machine Learning” is a wonderful to get started with mathematics specific to machine learning. It takes you through all the topics step-by-step with examples for illustrations. The first part covers essential Math topics theoretically and the second part covers more of ML-specific Math.
👨🏽🎓 Lectures from Jeffrey Chasnov — YouTube Channel
Jeffrey Chasnov is a professor at The Hong Kong University of Science and Technology in Mathematics. His YT channel also includes videos on numerical methods, differential equations and matrix algebra. He explains concepts starting with an example, so that they are clear and concise in the learner’s mind.
👨🏽🏫 MathTheBeautiful — YouTube Channel
MathTheBeautiful covers a number of topics, covering almost all of Math, just like Khan Academy. Concepts are beautifully explained with intuition to have concrete ideas on those concepts.
🎓 Ben Lambert’s YouTube Channel
Ben Lambert’s YT Channel covers advanced topics in Probability and Statistics. He also provides a number of lectures on Econometrics as well.
More from the Author
The End!
Do you know some other resources which can be featured here ( and are free as well )? Let me know at equipintelligence@gmail.com or in the comments section.
Hope you liked this short story! Thank You and have a nice day ahead!
