Fix local mdBook serving issues and expand learning resources

book.toml

@@ -1,13 +1,12 @@
 [book]
 authors = ["daemon1024"]
 language = "en"
-multilingual = false
 src = "src"
 title = "Minerva"
 
 [output.html]
 default-theme = "dark"
 preferred-dark-theme = "navy"
 git-repository-url = "https://github.com/osdc/minerva"
-git-repository-icon = "fa-github"
+git-repository-icon = "fab-github"
 edit-url-template = "https://github.com/osdc/minerva/edit/master/{path}"

src/SUMMARY.md

@@ -11,6 +11,7 @@
   - [How to decide](./lang/how_to_decide.md)
   - [JavaScript](./lang/js.md)
   - [Go](./lang/go.md)
+  - [Python](./lang/python.md)
   - [Rust](./lang/rust.md)
   - [Elixir](./lang/elixir.md)
 

src/lang/python.md

@@ -0,0 +1,21 @@
+# Python
+### Beginners' Tutorials
+- [Official Python Tutorial](https://docs.python.org/3/tutorial/)
+- [Ultimate Python Study Guide](https://github.com/huangsam/ultimate-python)
+
+### Video Tutorials
+- [FreeCodeCamp.org's Python Tutorial for Beginners ](https://www.youtube.com/watch?v=qwAFL1597eM)
+- [Harvard CS50’s Introduction to Programming with Python](https://www.youtube.com/watch?v=nLRL_NcnK-4)
+- [CodeWithHarry's Python Playlist](https://www.youtube.com/playlist?list=PLu0W_9lII9agwh1XjRt242xIpHhPT2llg)
+
+### Docs
+- [Official Documentation](https://docs.python.org/3/)
+- [NumPy Documentation](https://numpy.org/doc/)
+- [Pandas Documentation](https://pandas.pydata.org/docs/user_guide/index.html)
+- [Matplotlib Documentation](https://matplotlib.org/stable/index.html)
+- [SciPy Documentation](https://docs.scipy.org/doc//scipy/index.html)
+
+### Writting Good Python
+- [PEP 8 – Style Guide for Python Code](https://peps.python.org/pep-0008/)
+- [Raymond Hettinger - Beyond PEP 8 -- Best practices for beautiful intelligible code - PyCon 2015](https://www.youtube.com/watch?v=wf-BqAjZb8M)
+- [Write More Pythonic Code – Real Python](https://realpython.com/learning-paths/writing-pythonic-code/)

src/tech/aiml.md

@@ -1,5 +1,40 @@
 # AI/ML
+**Notes are brief qualitative descriptions to help with course selection.**
 
+### YouTube Courses
 - [Stanford CS229 by Andrew Ng](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)
+    - Classical Machine Learning. A good point to start with
+- [Neural Networks: Zero to Hero - Andrej Karpathy](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)
+    - More focused towards DL and Transformers, might not be for absolute beginners
+
+### Other video courses platforms
+- [Machine Learning with Scikit-learn, PyTorch & Hugging Face | Coursera](https://www.coursera.org/specializations/machine-learning-introduction)
+    - A classic material for learning machine learning. Complete and polished.
+- [Practical Deep Learning for Coders - Practical Deep Learning](https://course.fast.ai/)
+    - Has more of a modern feel and leans more towards DL. Not a classical ML course
+- [Deep Learning by Andrew Ng](https://www.coursera.org/specializations/deep-learning)
+    - A very popular course on Deep Learning and is recommended by many people. A very good course if your main goal is Neural Networks
 - [Machine Learning by freeCodeCamp](https://www.freecodecamp.org/learn/machine-learning-with-python#tensorflow)
-- [Deep Learning by Andrew Ng](https://www.coursera.org/specializations/deep-learning)
+
+### Text Based Courses
+- [microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all](https://github.com/microsoft/ML-For-Beginners?WT.mc_id=academic-105485-koreyst)
+    - Still updated and maintained till this date (date of writing this wiki).
+Personal Bias. Text based guides are much better than video courses
+- [Machine Learning  |  Google for Developers](https://developers.google.com/machine-learning/crash-course)
+    - Seems very practical. Good for a crash course.
+    - Can be used as reference or for quick overview of topics like regression, classification etc.
+- [Home - Made With ML by Anyscale](https://madewithml.com/)
+    - Non-traditional
+    - Good while making a proper **ML project**
+
+### Books for ML
+- [Mathematics for Machine Learning | Companion webpage to the book “Mathematics for Machine Learning”. Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press.](https://mml-book.github.io/)
+    - Is like Handbook of CP.
+    - good for going through **quickly**. While building intuition leveraging different resources
+- [Understanding Deep Learning](https://udlbook.github.io/udlbook/)
+    - DL resource
+
+### Misc
+- [Hugging Face - Learn](https://huggingface.co/learn)
+    - They have very specific courses. Not complete course. But very high quality imo.
+- [Deep Learning Online Courses | NVIDIA](https://www.nvidia.com/en-in/training/online/)