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50+ Free Resources To Learn Mathematics For Machine Learning

Why people hate Maths? If anyone wants to share their views on this question, please share it by tagging us (@TheInsaneApp) wherever you find this post. If you hate maths or not, this reasonable and resourceful guide is for you. In this post, We’ve curated the brain-friendly and best free resources to learn essential mathematics for machine learning. Resources covered in this post includes websites, books, free courses, cheat sheets, pdf, github repos and youtube playlist for linear algebra, calculus, probability, statistics, optimization methods and a lot more.

Let’s take a deep dive into each topic…

Best Free Books, Courses, Github Repos And Cheat Sheets Required To Learn Essential Mathematics For Machine Learning

Maths For machine Learning

If you want to learn all the topics from a single book or a single course then the below given resources are the best for you.

Maths behind Machine Learning – (Resources for Algebra, Trigonometry, Calculus, Probability, Statistics, Optimization and more, everything at one place)

1. Mathematics for Machine Learning (eBook)

2. Maths of Machine Learning by MIT (Course)

3. Linear Algebra, Calculus and Probability of Machine Learning (YouTube Playlist)

4. Maths for Machine Learning (YouTube Playlist)

5. Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning (eBook)

And If you want to learn a specific topic in Maths for Machine Learning then go through the list given below

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Linear Algebra For Machine Learning

Linear Algebra For Machine Learning

Why you should learn linear algebra for machine learning?

In machine learning, most of the time we deal with scalars and vectors, and matrices. For example in logistic regression, we do vector-matrix multiplication. Sometimes we do clustering of input by using spectral clustering techniques, and for this, we need to know eigenvalues and eigenvectors. Linear algebra is also used in data preprocessing, data transformation, dimensionality reduction, and model evaluation.

What are some of the core topics you should learn in linear algebra?

Topics such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Eigen decomposition of a matrix, LU Decomposition, Symmetric Matrices, Matrix Operations, Projections, Eigenvalues & Eigenvectors, Vector Spaces and Norms are needed for understanding the optimization methods used for machine learning

What are some of best free resources to Learn Linear Algebra for Machine Learning?

Best Websites To Learn Linear Algebra For Machine Learning
6. Learn Algebra for Machine Learning with Math is Fun
7. Linear Algebra with the Learning Machine
8. Introduction to Linear Algebra for Applied Machine Learning with Python
Best YouTube Tutorials To Learn Linear Algebra For Machine Learning
9. Essence of Linear Algebra by 3Blue1Brown
10. Trigonometry by Khan Academy
11. Linear Algebra by Dr Trefor Bazett
12. Trigonometry Fundamentals by 3Blue1Brown
13. Linear Algebra for Machine Learning By Applied AI Course
Best Courses To Learn Linear Algebra For Machine Learning
14. Linear Algebra for Machine learning by Khan Academy
16. Gilbert Strang lectures on Linear Algebra (MIT)
17. Coding The Matrix: Linear Algebra Through Computer Science Applications
Best Books To Learn Linear Algebra For Machine Learning (PDF)
18. Linear Algebra Abridged by Sheldon Axler
19. Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares
20. Linear Algebra for Machine Learning PDF
21. Deep Learning Book By Ian Goodfellow and Yoshua Bengio and Aaron Courville
Best Linear Algebra Cheat Sheet For Machine Learning
22. Matrix Calculus Cheat Sheet by Stanford University
23. Trigonometry Cheats by Paul Dawkins
24. Linear Algebra Cheat Sheet by Paul Dawkins

Probability And Statistics For Machine Learning

Probability for Machine Learning

What’s the use of probability and statistics in machine learning?

Probability helps you to manage the uncertainty. Uncertainty means working with imperfect or incomplete information. And in Machine Learning, we build predictive models from uncertain data. But we can manage uncertainty using the tools of probability. Whereas Statistics help you to count well, normalize well, obtain distributions, find out the mean of your input feature, and its standard deviation. That’s why knowledge of Probability and Statistics is important for machine learning.

What are some of the core topics you should learn in stats and probability?

Some of the fundamental Statistical and Probability Theory needed for ML are Combinatorics, Probability Rules & Axioms, Bayes’ Theorem, Random Variables, Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian), Moment Generating Functions and more.

What are some of the best free resources to learn Probability and Statistics?

Best Websites To Learn Probability And Statistics For Machine Learning
25. Seeing Theory – A Visual Introduction to Probability and Statistics
26. Learn Probability and Statistics of Machine Learning with Math is Fun
Best YouTube Tutorials To Learn Probability And Statistics For Machine Learning
27. Statistics 110 – Probability by Harvard University
28. Introduction to Probability by MIT
Best Courses To Learn Probability And Statistics For Machine Learning
29. Introduction to Statistics by Udacity
30. Probabilistic Systems Analysis and Applied Probability by MIT
31. Statistics and Probability for Machine Learning by Khan Academy
Best Books To Learn Probability And Statistics For Machine Learning (PDF)
32. Introduction to Probability
33. An Introduction to Statistical Learning for Machine Learning
34. Probability Theory: The Logic of Science by E. T. Jayne
35. The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman
Best Probability And Statistics Cheat Sheets For Machine Learning
36. A Concrete Introduction to Probability Using Python By Peter Norvig
37. Probability and Statistics Cheat Sheets for Machine learning by Stanford university
38. Statistics Cheats by MIT

Calculus For Machine Learning

Calculus For Machine Learning

What’s the use of calculus in machine learning?

Calculus helps us to explain the relationships between input and output variables. And Multivariate Calculus comes into the picture when you deal with a lot of features and huge data. That’s why familiarity with multivariate calculus is essential for building a machine learning model.

What are some of the core topics you should learn in calculus?

Some of the necessary topics include Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions, Directional Gradient, Hessian, Jacobian, Laplacian and Lagragian Distribution.

What are some of the best free resources to learn Calculus?

Best Websites To Learn Calculus For Machine Learning
39. Learn Calculus for machine Learning with The Learning Machine
40. Learn Calculus with Math is Fun
Best YouTube Playlists To Learn Calculus For Machine Learning
41. Essence of Calculus by 3Blue1Brown
42. Calculus 1, Calculus 2, Calculus 3 and Calculus 4
43. Single Variable Calculus by Penn Professor Robert Ghrist
44. Mathematics of Machine Learning – Multivariate Calculus by Imperial College London
Best Courses To Learn Calculus For Machine Learning
45. Single Variable Calculus by MIT
46. Multivariable Calculus by MIT
Best Books To Learn Calculus For Machine Learning (PDF)
47. Calculus by Gilbert Strang
48. Introduction to Calculus Volume I and Volume II by J.H. Heinbockel
49. Calculus Cheat Sheet by Paul Dawkins

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Optimizations Methods & Other Topics For ML

Optimization Methods For Machine Learning

What’s the use of optimization in machine learning?

Optimization methods are important to understand the computational efficiency and scalability of our Machine Learning Algorithm. In the end, mostly all Machine learning algorithms come down to some optimization tasks.

What are some core topics you should learn in optimization methods?

Knowledge of data structures (Binary Trees, Hashing, Heap, Stack etc), Dynamic Programming, Randomized & Sublinear Algorithm, Graphs, Gradient/Stochastic Descents and Primal-Dual methods are needed.

What are some of the best free resources to learn Optimization and other remaining topics?

Best Website To Learn Optimization Methods And Other Machine Learning Topics
50. Learn Optimization Methods of Machine Learning with the Learning Machine
Best YouTube Playlist To Learn Optimization Methods And Other Machine Learning Topics
51, How Optimization for Machine Learning Works
52. Optimization for Machine learning by DeepMind
Best Courses And Books To Learn Optimization Methods And Other Machine Learning Topics:
53. Optimization Methods for Machine Learning
54. Convex Optimization for Machine learning
55. A Survey of Optimization Methods from a Machine Learning Perspective

Despite the immense possibilities of Machine Learning and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. For that reason, we have curated and shared some of the best resources to learn essential mathematics for machine learning. We hope these curated list of resources for learning machine learning math will be helpful to you. So, that’s it for now. If you have any doubt or questions or suggestion, feel free to share your it with us wherever you’re following us.