> For the complete documentation index, see [llms.txt](https://irosyadi.gitbook.io/irosyadi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://irosyadi.gitbook.io/irosyadi/course/free-online-course.md).

# Free Online Course

## Online Course Platform

* [Coursera](https://www.coursera.org/)
* [Edx](https://www.edx.org/)

## List of Free Online Course

* [Resume Worded](https://resumeworded.com/free-online-courses/)
* [Class Central](https://www.classcentral.com/collection/top-free-online-courses)
  * [Providers](https://www.classcentral.com/providers)
* [Free Udemy Courses](https://www.udemyfreebies.com/)
* [Course List by Abakcus](https://abakcus.com/courses/)
* [Course List by Brilliant](https://brilliant.org/courses/)
* [Course List by Tombasche](https://github.com/tombasche/professional-development)

## Hacking Satellite Course

* [Crash Course for Hacking Satellite](https://nyan-sat.com/chapter0.html)

## Machine Learning Course

* [Machine Learning University from Amazon](https://www.youtube.com/channel/UC12LqyqTQYbXatYS9AA7Nuw/playlists), [Information from Amazon](https://www.amazon.science/latest-news/machine-learning-course-free-online-from-amazon-machine-learning-university?es_id=8aea00348c) but these are mostly designed around Amazon products and do not teach much actual ML
* [DEEP LEARNING Course Yann LeCun & Alfredo Canziani](https://atcold.github.io/pytorch-Deep-Learning/) MATERIAL [Google Drive](https://bitly.com/DLSP20), [Notebooks](https://github.com/Atcold/pytorch-Deep-Learning) [NYU Site](https://cds.nyu.edu/deep-learning/)
* [AI Course: Elements of AI](https://www.elementsofai.com/)
* [Complete ML Coursework](https://github.com/AbhishekSinhaCoder/Complete-ML-Coursework)

## Programming

* [Mike Dane](https://www.mikedane.com)
* [Learn Programming](https://learnprogramming.online/)

## Deep Learning Course

* [DeepCourse](https://arthurdouillard.com/deepcourse/)
* [Deep Learning by deeplearning.ai - Coursera](https://www.coursera.org/specializations/deep-learning)
* [Yann LeCun’s Deep Learning Course at CDS–NYU Center for Data Science](https://cds.nyu.edu/deep-learning/)
* [briandalessandro/DataScienceCourse: This holds iPython notebooks and lecture slides for the Intro to Data Science Master's course I teach at NYU.](https://github.com/briandalessandro/DataScienceCourse)

## Time Series Course

* [Welcome to STAT 510! - STAT 510](https://online.stat.psu.edu/stat510/)

## Machine Learning Course

* [DeepCourse](https://arthurdouillard.com/deepcourse/) License: Apache

## Linear Algebra

* [MATH 42201/52201](https://www.math.kent.edu/~reichel/courses/intr.num.comp.1/syllabus.html)
* [Linear Algebra 2019 Fall](https://speech.ee.ntu.edu.tw/~hylee/la/2019-fall.html)

## Electrical Circuit Course Notes

* [Circuit 2014 Fall](https://speech.ee.ntu.edu.tw/~hylee/circuit/2014-fall.html)

## Machine Learning Course Notes

* [ML 2021 Spring](https://speech.ee.ntu.edu.tw/~hylee/ml/2021-spring.html) [ML 2020 Spring](https://speech.ee.ntu.edu.tw/~hylee/ml/2020-spring.html) [Introduction—Learning Machine](https://rentruewang.github.io/learning-machine/intro.html) [rentruewang/learning-machine: A handbook for machine learning](https://github.com/rentruewang/learning-machine) License: GPL
* [Best tutorials, courses, and blog posts](https://tutobase.com/)
* [Learn - AI Campus](https://ki-campus.org/overview)\
  ![](https://teddit.net/pics/w:null_iB9q4pekrmxkPbOn_I_ImNveW0FWcWu9_lZYR0miQ1M.jpg)

## Course

* [microsoft/IoT-For-Beginners: 12 Weeks, 24 Lessons, IoT for All!](https://github.com/microsoft/IoT-For-Beginners) License: MIT

## Course

* [Introduction to Deep Learning](https://sebastianraschka.com/blog/2021/dl-course.html)

## Machine Learning

* [Machine Learning Crash Course - Google Developers](https://developers.google.com/machine-learning/crash-course/)
* [MLOps Course - Made With ML](https://madewithml.com/) License MIT

## Course

* [Full stack open 2021](https://fullstackopen.com/en/) License: CC-BY-NC-SA

## HTML Learn

* [Don't Panic–It's Only HTML (Crash Course For Beginners) - YouTube](https://www.youtube.com/watch?v=3939sZ20kPk)
* [HTML Crash Course For Absolute Beginners - YouTube](https://www.youtube.com/watch?v=UB1O30fR-EE)
* [CSS Crash Course For Absolute Beginners - YouTube](https://www.youtube.com/watch?v=yfoY53QXEnI)

## Course

* [MIT Open Learning Library - Open Learning](https://openlearning.mit.edu/courses-programs/open-learning-library) License: CC-BY-NC-SA

## Course

* [MIT 6.874/6.802/20.390/20.490/HST.506: Computational Systems Biology: Deep Learning in the Life Sciences - Spring 2021 - YouTube](https://www.youtube.com/playlist?list=PLUgbVHjDhargXdgtc1ZcJb2lz20msuBfS)

## Programming Course

* [CMU CS Academy](https://academy.cs.cmu.edu/)

## Machine Learning Course

* [Teaching - CS 229](https://stanford.edu/~shervine/teaching/cs-229/)
* [SEE Standford CS 229](https://see.stanford.edu/Course/CS229) CC-BY-NC
* [aidysft](https://sites.google.com/site/aidysft/)
* [Designing, Visualizing and Understanding Deep Neural Networks](https://bcourses.berkeley.edu/courses/1453965/) : Public Domain
* [Deep learning courses at UC Berkeley - berkeley-deep-learning.github.io](https://berkeley-deep-learning.github.io/)
* [CS 189/289A: Introduction to Machine Learning](https://people.eecs.berkeley.edu/~jrs/189/)
  * [Canvas LMS](https://bcourses.berkeley.edu/courses/1503419)
  * [Vojta Molda / berkeley-cs189-introduction-to-ml · GitLab](https://gitlab.com/vojtamolda/berkeley-cs189-introduction-to-ml)
  * [CS 189](https://www.eecs189.org/)

## Machine Learning

* Google’s [ML crash course](https://developers.google.com/machine-learning/crash-course)
* [TensorFlow tutorials](https://www.tensorflow.org/tutorials/)
* [PyTorch tutorials](https://pytorch.org/tutorials/)
* FastAI course: [Practical Deep Learning for Coders](https://course.fast.ai/)
* [Full Stack Deep Learning](https://course.fullstackdeeplearning.com/) course
* [A Software Engineer’s trek into Machine Learning - Towards Data Science](https://towardsdatascience.com/software-engineers-trek-into-machine-learning-46b45895d9e0)

## Machine Learning Course

* [NeuromatchAcademy/course-content: Summer course content for Neuromatch Academy](https://github.com/NeuromatchAcademy/course-content)

## Machine Learning Course

* [microsoft/ML-For-Beginners: 12 weeks, 24 lessons, classic Machine Learning for all](https://github.com/microsoft/ML-For-Beginners) License: MIT
* [Machine Learning Crash Course - Google Developers](https://developers.google.com/machine-learning/crash-course)
* [Machine Learning University](https://aws.amazon.com/machine-learning/mlu/)

## Open Course

* [The Missing Semester of Your CS Education · the missing semester of your cs education](https://missing.csail.mit.edu/) License: CC-NC

## NLP Course

* [Transformer models - Hugging Face Course](https://huggingface.co/course/chapter1)

## Course

* [CS 329S - Machine Learning Systems Design](https://stanford-cs329s.github.io/index.html)
* [CS230 Deep Learning](https://cs230.stanford.edu/)

## Course

* [Lesson Directory - Programming Historian](https://programminghistorian.org/en/lessons/) Digital Tools for Humanity, License: CC-BY

## Course

* [Applied Compositional Thinking for Engineers–Applied Compositional Thinking for Engineers](https://applied-compositional-thinking.engineering/)

## Distributed Systems

* [Distributed Systems Course](https://www.distributedsystemscourse.com/)

## Course

* [lijqhs/deeplearning-notes: Notes for Deep Learning Specialization Courses led by Andrew Ng.](https://github.com/lijqhs/deeplearning-notes) License: MIT
* <https://web.stanford.edu/~jurafsky/NLPCourseraSlides.html> Lecture Slides from the 2012 Stanford Coursera course
* [ISLR Textbook Slides, Videos and Resources](https://fs2.american.edu/alberto/www/analytics/ISLRLectures.html) Introduction to Statistical Learning: With Applications in R Lecture Slides and Videos
* [Entire Computer Science Curriculum in 1000 YouTube Videos - Laconicml](https://laconicml.com/computer-science-curriculum-youtube-videos/)
  * [Computer Science Currriculum in 1000 Videos (no Ads)](https://cs1000.vercel.app/)

## Fast.ai Online Course

* [Practical Deep Learning for Coders](https://course.fast.ai/)
* [Part 2: Deep Learning from the Foundations](https://course19.fast.ai/part2)
* [Practical Data Ethics](https://ethics.fast.ai/)
* [Computational Linear Algebra](https://github.com/fastai/numerical-linear-algebra/blob/master/README.md)
* [Code-First Introduction to Natural Language Processing](https://www.fast.ai/2019/07/08/fastai-nlp/)

## Course

* [CSC321 Intro to Neural Networks and Machine Learning Winter 2018](https://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/), also:
  * [CSC311 Fall 2020](https://www.cs.toronto.edu/~rgrosse/courses/csc311_f20/)
  * [CSC2515 Fall 2019](https://www.cs.toronto.edu/~rgrosse/courses/csc2515_2019/)
  * [CSC421/2516 Winter 2019](https://www.cs.toronto.edu/~rgrosse/courses/csc421_2019/)

## Computer Science

* [Developer-Y/cs-video-courses: List of Computer Science courses with video lectures.](https://github.com/Developer-Y/cs-video-courses)

## Full Stack Deep Learning

* [Full Stack Deep Learning - Full Stack Deep Learning](https://course.fullstackdeeplearning.com/)

## Course Self Taught

* [Teach Yourself Computer Science](https://teachyourselfcs.com/)
* [ossu/data-science: Path to a free self-taught education in Data Science!](https://github.com/ossu/data-science)
* [ossu/computer-science: Path to a free self-taught education in Computer Science!](https://github.com/ossu/computer-science)

## Course

[Introduction to Reinforcement Learning with David Silver](https://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver) ([deepmind.com](https://news.ycombinator.com/from?site=deepmind.com))

* [Natural Language Processing (NLP) for Semantic Search - Pinecone](https://www.pinecone.io/learn/nlp/)