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. python programming (DONE!)
2. Python (I've already took FCC so, I'm DONE!)
3. Study ML Microsoft
4. Discrete Math
5. Python projects and certifications, kaggle (free)

Starter (using TF)

1. ML Visualization
2. Course offered by Google
3. Tutorial

Learn (PyTorch)

1. Tutorials

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. Full stack deep Learning, 2022 (For full stack, recommendeded by an Expert)

Free Certification (Deadline: 27th February 2024)

1. HuggingFace Tutorial series and excersise

Visualization

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

Math Concepts (Deadline: I still think about it)

1. Khan Academy, Algebra1, Alg 2, Alg 3, Alg-Trig, Trig, Precalculus
2. Essence of calculus, 3blue1brown
3. Essence of Linear Algebra, 3blue1brown
4. MIT OpenLearningLib, Cal 1, 2, 3,Lin-Alg, Matrix Method and Data Analysis, ML
5. Multi Variable Calculus
6. Linear Algebra
7. Convex Optimization
8. Math for Deep Learning
9. Linear Algebra, Fast.Ai
10. Another Linear Algebra
11. Bayesian for Hackers

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

1. Really Advance
2. NN DL

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

1. Deep Learning Introduction
2. Deep learning 1
3. Deep learning 2
4. Taught by Yan Lecun
5. Machine Learning With Graphs
6. Convolutional Neural Networks
7. Reinforcement Learning

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

1. Computer Graphics NYU
2. Geometry Processing
3. Rendering Book
4. Computer Vision
5. Multiple View Geometry
6. Machine Learning for 3D Vision
7. Advance Computer Vision
8. Photogrammetry I & II Course
9. Photogrammetry Course/Agisoft-SPbU
10. Computer Vision Book
11. Cool Tutorials
12. GPU mesh (I'm not sure)

HPC Course

1. HPC
2. Learn OpenMP and PThreads

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

1. Self Driving Cars
2. Mobile Sensing and Robotics I
3. Mobile Sensing and Robotics II
4. MIT Robotics
5. 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