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Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
| Date | Stars |
|---|---|
| 2026-07-24 | 4813 |
| 2026-07-25 | 4814 |
| 2026-07-28 | 4814 |
| 2026-07-30 | 4814 |
| 2026-07-31 | 4819 |
| 2026-08-06 | 4826 |
Today
+7 stars today
This week
+12 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.25%/day
# Machine Learning Foundations
This repo is home to the code that accompanies Jon Krohn's *Machine Learning Foundations* curriculum, which provides a comprehensive overview of all of the subjects — across mathematics, statistics, and computer science — that underlie contemporary machine learning approaches, including deep learning and other artificial intelligence techniques.
There are eight subjects in the curriculum, organized into four subject areas. See the "Machine Learning House" section below for detail on why these are the essential foundational subject areas:
* **Linear Algebra**
* 1: [Intro to Linear Algebra](https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/1-intro-to-linear-algebra.ipynb)
* 2: [Linear Algebra II: Matrix Operations](https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/2-linear-algebra-ii.ipynb)
* **Calculus**
* 3: [Calculus I: Limits & Derivatives](https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/3-calculus-i.ipynb)
* 4: [Calculus II: Partial Derivatives & Integrals](https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/4-calculus-ii.ipynb)
* **Probability and Statistics**
* 5: [Probability & Information Theory](https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/5-probability.ipynb)
* 6: [Intro to Statistics](https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/6-statistics.ipynb)
* **Computer Science**
* 7: [Algorithms & Data Structures](https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/7-algos-and-data-structures.ipynb)
* 8: [Optimization](https://github.com/jonkrohn/ML-foundations/blob/master/notebooks/8-optimization.ipynb)
Later subjects build upon content from earlier subjects, so the recommended approach is to progress through the eight subjects in the order provided. That said, you're welcome to pick and choose individual subjects based on your interest or existing familiarity with the material. In particular, each of the four subject areas are fairly independent so could be approached separately.
### Where and When
The eight *ML Foundations* subjects were initially offered by [Jon Krohn](jonkrohn.com) as live online trainings in the [O'Reilly learning platform](https://learning.oreilly.com/home/) from May-Sep 2020 (and were offered a second time from Jul-Dec 2021; see [here](https://www.jonkrohn.com/talks) for individual lecture dates).
To suit your preferred mode of learning, the content is now available via several channels:
* **YouTube**
* Linear Algebra [complete playlist here](https://www.youtube.com/playlist?list=PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a) and [detailed blog post here](https://www.jonkrohn.com/posts/2021/5/9/linear-algebra-for-machine-learning-complete-math-course-on-youtube)
* Calculus [complete playlist here](https://www.youtube.com/playlist?list=PLRDl2inPrWQVu2OvnTvtkRpJ-wz-URMJx)
* [Probability playlist](https://www.youtube.com/playlist?list=PLRDl2inPrWQWwJ1mh4tCUxlLfZ76C1zge) is in active development (sign up for my email newsletter at [jonkrohn.com](https://www.jonkrohn.com/) to be notified of new video releases)
* In time, all of the subjects of my ML Foundations curriculum will be freely available on YouTube.
* **O'Reilly** (many employers and educational institutions provide free access to this platform; if you don't have access, you can get a 30-day free trial [via my special SDSPOD23 code](https://learning.oreilly.com/get-learning/?code=SDSPOD23))
* [Linear Algebra videos](https://learning.oreilly.com/videos/linear-algebra-for/9780137398119/) published in Dec 2020 ([free hour-long lesson](https://www.youtube.com/watch?v=uG_wjmuigGg))
* [Calculus videos](https://learning.oreilly.com/videos/calculus-for-machine/9780137398171/) published in Jan 2021 ([free hour-long lesson](https://youtu.be/ZDAX17OGMAM))
* [Probability and Stats videos](https://learning.oreilly.com/videos/probability-and-statistics/97801375662Excerpt of 11,135 characters
Read on GitHub
Jon Krohn · Y Carrot · United States
495
Pietro Monticone · Harmonic · United States
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:274a6557b39f51c7, topic:pytorch, topic:tensorflow