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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Complete path for a beginner to become a Machine Learning Scientist!
| Date | Stars |
|---|---|
| 2026-07-31 | 350 |
| 2026-08-06 | 350 |
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## Machine Learning & Deep Learning Curriculum
Loose path:
1. Math
2. Programming
3. Machine Learning concepts
4. Specializations
### Math
Understanding Math is pivotal. You can never be a good Machine Learning Scientist
by skipping the Math.
1. [Probability & Statistics](https://www.khanacademy.org/math/probability)
Basic Probability and Stats will be helpful in understanding ML algorithms like Naive Bayes.
2. [Statistics 101 - Udacity](https://www.udacity.com/course/intro-to-statistics--st101)
Taught by the founder of GoogleX it's full of exercises in Python so you won't get bored.
3. [MIT 18.06 Linear Algebra](https://www.youtube.com/watch?v=ZK3O402wf1c&list=PLE7DDD91010BC51F8)
Prof. Strang is terrific! Not only he'll make you fall in love in Linear Algebra but you'll learn
important concepts like SVD and matrix algebra. You might wanna grab this [PDF](http://www.math.hcmus.edu.vn/~bxthang/Linear%20algebra%20and%20its%20applications.pdf)
as well. Be sure to also solve the exam question papers from here: [link](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/exams/)
4. [MIT Single Variable Calculus](https://www.youtube.com/watch?v=7K1sB05pE0A&list=PL590CCC2BC5AF3BC1)
This is my personal favorite book, use it for SVC + MVC [link](https://drive.google.com/open?id=0BwEXorNDIEnFc3VKN3RUOWdRdUE)
Amazing course but it gets quite tedious in the middle, you might wanna skim some geometry, but the key is
to understand how optimization works. Be sure to solve questions from here: [link](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/exams/)
5. [MIT Multi Variable Calculus](https://www.youtube.com/watch?v=PxCxlsl_YwY&list=PL4C4C8A7D06566F38)
Understanding vector calculus is necessary for algorithms like SVM, you might wanna skim some parts
which are purely theoretical. Be sure to solve questions from here: [link](https://ocw.mit.edu/courses/mathematics/18-02-multivariable-calculus-fall-2007/exams/)
7. (Optional) [Stanford Convex Optimization](https://lagunita.stanford.edu/courses/Engineering/CVX101/Winter2014/about)
WARNING: Do this course only if you're very good at math. Convex Optimization will teach you numerous
functions used in Machine Learning. But this course is extremely heavy on Math!
### Introduction to Programming & Algorithms
* Python - Any one, both courses are equally good
1. [Complete Python BootCamp](https://www.udemy.com/complete-python-bootcamp/)
2. [Complete Python Masterclass](https://www.udemy.com/python-the-complete-python-developer-course/)
* Algorithms
Since you'll be coding a lot of algorithms yourself basic understanding is necessary
1. [Basic Algorithms and Complexity Theory](https://www.youtube.com/watch?v=o4SGkB_8fFs&list=PLhQjrBD2T382VRUw5ZpSxQSFrxMOdFObl)
In case you want to go deeper
1. [Algorithms Stanford I](http://online.stanford.edu/course/algorithms-design-and-analysis-part-1)
2. [Algotithms Stanford II](http://online.stanford.edu/course/algorithms-design-and-analysis-part-2)
### Introduction to Machine Learning
1. [Machine Learning by Andrew Ng](https://www.coursera.org/learn/machine-learning)
A must do course, best course of Introduction to Machine Learning so far, light on Math and focuses more on concepts.
Complete one out of two:
1. [Machine Learning A-Z](https://www.udemy.com/machinelearning/)
Introductory course on ML focusing on not only Python but also R, one of the best sellers on Udemy.
2. [Introduction to Machine Learning - Udacity](https://www.udacity.com/course/intro-to-machine-learning--ud120)
Sebastian Thrun does an awesome job explaining various approaches in ML. It gets a little boring in the middle
but overall it's very good.
### Applied Machine Learning
Two quick courses on applying the theory you learnt. They're shortExcerpt of 6,646 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:353f17cf47137cdf, topic:deep-learning