metacritical · July 16, 2025 14:36 · Feb 8, 2025 · Feb 8, 2025 · Feb 8, 2025 · Feb 8, 2025
diff --git a/ML_Crash_course.md b/ML_Crash_course.md
@@ -6,7 +6,7 @@
 
 ## Foundational Level
 ### Mathematics & Statistics
-- [x] Linear Algebra Fundamentals
+- [ ] Linear Algebra Fundamentals
   - Course: [3Blue1Brown Linear Algebra Series](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
   - Course: MIT OpenCourseWare 18.06 Linear Algebra
   - Topics: Vectors, matrices, eigenvalues, matrix operations

diff --git a/ML_Crash_course.md b/ML_Crash_course.md
@@ -11,7 +11,7 @@
   - Course: MIT OpenCourseWare 18.06 Linear Algebra
   - Topics: Vectors, matrices, eigenvalues, matrix operations
 
-- [x] Statistics & Probability
+- [ ] Statistics & Probability
   - Course: [StatQuest with Josh Starmer](https://www.youtube.com/c/joshstarmer)
   - Resource: Khan Academy Statistics & Probability
   - Topics: Distributions, hypothesis testing, confidence intervals

diff --git a/ML_Crash_course.md b/ML_Crash_course.md
@@ -11,7 +11,7 @@
   - Course: MIT OpenCourseWare 18.06 Linear Algebra
   - Topics: Vectors, matrices, eigenvalues, matrix operations
 
-- [ ] Statistics & Probability
+- [x] Statistics & Probability
   - Course: [StatQuest with Josh Starmer](https://www.youtube.com/c/joshstarmer)
   - Resource: Khan Academy Statistics & Probability
   - Topics: Distributions, hypothesis testing, confidence intervals

diff --git a/ML_Crash_course.md b/ML_Crash_course.md
@@ -6,7 +6,7 @@
 
 ## Foundational Level
 ### Mathematics & Statistics
-- [ ] Linear Algebra Fundamentals
+- [x] Linear Algebra Fundamentals
   - Course: [3Blue1Brown Linear Algebra Series](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
   - Course: MIT OpenCourseWare 18.06 Linear Algebra
   - Topics: Vectors, matrices, eigenvalues, matrix operations

diff --git a/ML_Crash_course.md b/ML_Crash_course.md
@@ -0,0 +1,97 @@
+# Machine Learning Learning Path with Resources
+
+## Essential Books
+1. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
+2. "Build a Large Language Model from Scratch" - Bestseller on implementing LLMs
+
+## Foundational Level
+### Mathematics & Statistics
+- [ ] Linear Algebra Fundamentals
+  - Course: [3Blue1Brown Linear Algebra Series](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
+  - Course: MIT OpenCourseWare 18.06 Linear Algebra
+  - Topics: Vectors, matrices, eigenvalues, matrix operations
+
+- [ ] Statistics & Probability
+  - Course: [StatQuest with Josh Starmer](https://www.youtube.com/c/joshstarmer)
+  - Resource: Khan Academy Statistics & Probability
+  - Topics: Distributions, hypothesis testing, confidence intervals
+
+### Programming & Tools
+- [ ] Python for ML
+  - Course: [Python for Data Science - freeCodeCamp](https://www.freecodecamp.org/learn/data-analysis-with-python/)
+  - Libraries: NumPy, Pandas, Matplotlib tutorials
+  - Practice: Kaggle Learn Python & Pandas
+
+## Intermediate Level
+### Machine Learning Foundations
+- [ ] ML Basics
+  - Course: [Andrew Ng's Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction)
+  - Course: [fast.ai Practical Deep Learning](https://course.fast.ai/)
+  - Topics: Linear regression, logistic regression, decision trees
+
+### Deep Learning Fundamentals
+- [ ] Neural Networks
+  - Course: [3Blue1Brown Neural Networks Series](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
+  - Course: [Andrej Karpathy's Zero to Hero Series](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)
+  - Topics: Backpropagation, activation functions, optimization
+
+## Advanced Level
+### Modern Deep Learning
+- [ ] Transformer Architecture
+  - Paper: [Attention Is All You Need](https://arxiv.org/pdf/1706.03762)
+  - Video: [Yannic Kilcher's Transformer Explanation](https://www.youtube.com/watch?v=iDulhoQ2pro)
+  - Course: [Stanford CS25: Transformers United](https://web.stanford.edu/class/cs25/)
+
+### Large Language Models
+- [ ] Foundation Models
+  - Paper: [GPT-3 Paper](https://arxiv.org/pdf/2005.14165)
+  - Course: [Stanford's Foundation Models Course](https://stanford-cs324.github.io/winter2022/)
+  - Resource: [Full Stack Deep Learning](https://fullstackdeeplearning.com/)
+
+### Training & Fine-tuning
+- [ ] Advanced Training Methods
+  - Paper: [LoRA Paper](https://arxiv.org/abs/2106.09685)
+  - Paper: [RLHF Paper](https://arxiv.org/pdf/2203.02155)
+  - Resource: [Hugging Face Course](https://huggingface.co/learn)
+
+## Expert Level
+### Reasoning & Planning
+- [ ] Advanced AI Techniques
+  - Paper: [Chain of Thought Paper](https://arxiv.org/pdf/2201.11903)
+  - Paper: [Tree of Thoughts](https://arxiv.org/pdf/2305.10601)
+  - Paper: [ReACT Paper](https://arxiv.org/pdf/2210.03629)
+
+### Latest Research Areas
+- [ ] Cutting Edge Topics
+  - Resource: [Papers with Code](https://paperswithcode.com/)
+  - Resource: [arXiv Sanity Preserver](http://www.arxiv-sanity.com/)
+  - Follow: Leading AI Labs' Research Blogs (DeepMind, OpenAI, Anthropic)
+
+## Additional Resources
+### Comprehensive Resource Collections
+- [History of Deep Learning](https://github.com/adam-maj/deep-learning)
+- [a16z AI Canon](https://a16z.com/ai-canon/)
+- [Prompting Guide](https://www.promptingguide.ai/)
+- [2025 AI Engineer Reading List](https://www.latent.space/p/2025-papers)
+
+### Practice Platforms
+- [Kaggle Competitions](https://www.kaggle.com/competitions)
+- [Google Colab](https://colab.research.google.com/) (Free GPU access)
+- [Hugging Face Spaces](https://huggingface.co/spaces) (Deploy models)
+
+### Survey Papers
+- [LLM Survey (2024)](https://arxiv.org/pdf/2402.06196v2)
+- [Agent Survey (2023)](https://arxiv.org/pdf/2308.11432)
+- [Prompt Engineering Survey (2024)](https://arxiv.org/pdf/2406.06608)
+
+### Benchmarks
+- [BIG-Bench](https://arxiv.org/pdf/2206.04615)
+- [SWE-Bench](https://arxiv.org/pdf/2310.06770)
+- [Chatbot Arena](https://arxiv.org/pdf/2403.04132)
+
+Note: 
+1. Replace "[ ]" with "[x]" to mark topics as completed
+2. Follow the order as concepts build upon each other
+3. Practice with real projects alongside theoretical learning
+4. Join AI communities (Discord servers, Reddit r/MachineLearning, Twitter AI community)
+5. Keep up with latest developments through ML paper reading groups