personal-AI-journey

My personal path to learning machine learning is mostly free resources I've found on the internet, with a broad domain and interests. I suggest learning by order! (All free courses from my surfing web skills)

Learn Python and math first (Clear) (Deadline: 31th Nov 2024)

1. https://freecodecamp.com (python programming) (DONE!)
2. https://www.py4e.com/ (Python) (I've already took FCC so, I'm DONE!)
3. https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/the-ai-study-guide-azure-machine-learning-edition/ba-p/4063656?wt.mcid=studentamb335325 (Microsoft)
4. https://openstax.org/subjects/math (Please learn Algebra and Trigonometry, CALC I & II for MIT Math for CS, So you can understand the underlying concept of 6-042j and proceed to 6-006)
5. https://ggc-discrete-math.github.io/index.html#_course_objectives (Discrete Math)
6. https://ocw.mit.edu/courses/6-006-introduction-to-algorithms-spring-2020/pages/calendar/
7. https://www.kaggle.com/learn (Python projects and certifications) (free)

Starter (using TF)

1. https://mlu-explain.github.io/
2. https://developers.google.com/machine-learning/crash-course (Course offered by Google)
3. https://www.tensorflow.org/tutorials (Tutorial)

Learn (PyTorch)

1. https://pytorch.org/tutorials/ (Tutorials) (free)

Adding Tech stack

https://hpc.llnl.gov/documentation/tutorials (Learn OpenMP and PThreads)

Really Nice Huge Learning from all ML/DL

1. D2L.AI (Unofficial Indonesian Translation) (STILL ON PROGRESS!!)
2. Fast.ai Course (All algorithms implemented in PyTorch)
3. https://fullstackdeeplearning.com/course/2022/ (For full stack, recommendeded by an Expert)

Free Certification (Deadline: 27th February 2024)

1. https://huggingface.co/learn/audio-course/chapter0/introduction (Audio)
2. https://huggingface.co/learn/nlp-course/chapter0/1?fw=pt (NLP)
3. https://huggingface.co/learn/deep-rl-course/unit0/introduction (RL)

Visualization

1. Machine Learning University Explain
2. Stanford Lecture Notes simplified
3. Brown University Statistics

Math Concepts (Deadline: I still think about it)

1. https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr (free) (3blue1brown) (Essence of calculus)
2. https://openstax.org/subjects/math ( CALC I & II for Single variable calculus, CALC III for Multi-Variable Calculus, and statistics)
3. https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab (3blue1brown) (Essence of Linear Algebra)
4. https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/ (free) (Linear Algebra)
5. https://www.cs.utexas.edu/~flame/laff/alaff/ALAFF.html
6. https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/math-for-deep-learning/ (free)
7. https://github.com/fastai/numerical-linear-algebra/blob/master/README.md (free) (Linear Algebra)
8. https://pabloinsente.github.io/intro-linear-algebra

Advance Course and Book (Optional) (Deadline: I still think about it)

1. https://www.deeplearningbook.org/ (Really Advance)
2. http://neuralnetworksanddeeplearning.com/index.html (free)

Uni Lectures on Deep Learning (Deadline: I still think about it)

1. http://introtodeeplearning.com/2022/index.html (Deep Learning Introduction)
2. https://uvadlc.github.io/ (Deep learning 1)
3. https://uvadl2c.github.io/ (Deep learning 2)
4. https://cds.nyu.edu/deep-learning/ (Taught by Yan Lecun)
5. https://web.stanford.edu/class/cs224w/ (Machine Learning With Graphs)
6. http://cs231n.stanford.edu/ (Convolutional Neural Networks)
7. https://sites.google.com/view/berkeley-cs294-158-sp20/home (Reinforcement Learning) (2020)

Computer Graphics, Computer Vision and Photogrammetry (Deadline: I still think about it) (Not In Order)

1. https://cims.nyu.edu/gcl/teaching.html (Computer Graphics)
2. https://github.com/teseoch/Geometric-Modeling-Fall2021 (Geometry Processing)
3. https://www.pbr-book.org/ (Rendering Book)
4. http://16385.courses.cs.cmu.edu/spring2022/lectures (Computer Vision)
5. https://geometric3d.github.io/ (Multiple View Geometry)
6. https://learning3d.github.io/ (Machine Learning for 3D Vision)
7. https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/ (Advance Computer Vision)
8. https://www.ipb.uni-bonn.de/photo12-2021/ (Photogrammetry I & II Course)
9. https://github.com/PhotogrammetryCourse/ (In Russian, it's taught by Agisoft Metashape Developer!)
10. https://szeliski.org/Book/ (Computer Vision Book)
11. https://chahatdeep.github.io/tutorials.html#tutorials
12. https://ingowald.blog/pinned-pages/ //GPU mesh (I'm not sure)

Uni lectures (Robotics) (Deadline: I still think about it)

1. https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ (Self Driving Cars)
2. https://www.ipb.uni-bonn.de/msr1-2021/ (Mobile Sensing and Robotics I)
3. https://www.ipb.uni-bonn.de/msr2-2021/ (Mobile Sensing and Robotics II)
4. https://vnav.mit.edu/ (MIT Robotics)
5. https://github.com/gaoxiang12/slambook-en (SLAM)

Geomatics (Unordered list)

• TuDelft Course
• Potential MSc topics as well as Potential research orientation
• Personal Future Ideas

Following

1. Wei Xiao
2. Florent Poux

I don't Know if it is Important, But I feel I'm going to need it sooner or later

1. Cool repo 1