python-ml-resources.md

Collection of resources in Python

Hactoberfest-2021 Instructions:

Please edit this file by adding resource(s) you would recommend. Plase add your name, your GitHub username, and #hactoberfest-2021, somewhere above or below the item(s) you have added. For more instructions, please see:

• hactoberfest_2021_instructions.md
• issue #2, in "issues" tab, top-menu.

I. Classes

• Coursera, Andrew Ng instructor, Deep Learning Specialization
  • https://www.coursera.org/specializations/deep-learning?
    Five classes. Classes 1 neural networks and 4 convolutional neural networks are foundations.
• Udemy, Jose Portilla, Python for Data Science and Machine Learning Bootcamp
  • https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
• Fast.AI, Part 1: Practical Deep Learning for Coders, Part 2: Foundations
  • 2020 part 1 course: https://course.fast.ai/
  • 2019 part 2 course: https://course19.fast.ai/part2
• Hacktoberfest, Global event, October 1st to 31st annually: https://hacktoberfest.digitalocean.com/register
  • Lots of resources to help new users get involved with open source repos on GitHub. Help first timers with Git, GitHub and Pull Requests.

II. Tutorials

III. Books

III-a. Books for Beginners

• Introduction to Statistical Learning with Applications in R, James et al.
  Stanford professors, MOOC and video available from Lagunitas.
  Good beginner book on machine learning concepts. Has easy practice questions.
  Don't need to know R. Many Python versions of code is available from others.

III-b. Books for Intermediate ML and DL

• Python Data Science Handbook, Jake VanderPlas
  Teaches you how to ise main machine learning Python libraties. Not a core concepts book.
  Covers Jupyter Notebook, Numpy, Pandas, Matplotlib, Scikit-Learn, and a little bit of Unix bash commands.

  • github: https://github.com/jakevdp/PythonDataScienceHandbook

• Hands on Machine Learning, 2nd ed, Aurélien Géron

  • Early Release pdf: https://www.knowledgeisle.com/wp-content/uploads/2019/12/2-Aur%C3%A9lien-G%C3%A9ron-Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow_-Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-O%E2%80%99Reilly-Media-2019.pdf
  • github: https://github.com/ageron/handson-ml2

• Deep Learning with Python, 2nd ed., Fracois Chollet, c 2021 (nov est), Manning Co.

  • https://www.manning.com/books/deep-learning-with-python-second-edition
  • github: https://github.com/fchollet/deep-learning-with-python-notebooks
  • Added by Jennifer Yoon, github: JennEYoon, #hacktoberfest-2021

III-c. Books for Foundations

• Goodfellow et al., Deep Learning

  • link to pdf: https://www.deeplearningbook.org/front_matter.pdf
  • link to website: https://www.deeplearningbook.org/

• Magnus Ekman, NVIDIA Deep Learning Institute, Learning Deep Learning, c 2002, Addison-Wesley publication.

  • https://www.nvidia.com/en-us/training/books/

III-d. Related Books in Statistics & Probability, Algorithms, and Python Programming

• Algorithms Illuminated, books 1, 2, 3, 4, by Tim Roughgarden

  • MOOC class on Coursera.org: https://www.coursera.org/specializations/algorithms
  • YouTube has videos for all 4 parts: https://www.youtube.com/channel/UCcH4Ga14Y4ELFKrEYM1vXCg
  • Added by Jennifer Yoon, github: JennEYoon, #hacktoberfest-2021

• Statistical Rethinking
  Bayesian statistics.

IV. Conferences and Events

• SciPy Conferenc, annual in July
• PyData Conference, several times a year in different locations.
• PyCon Conference, annual in June
• Meetups - get moral support from other learners and build your network.
• Kaggle Competitions: https://www.kaggle.com/competitions
  • Best for building experience with professional-level deep learning projects.

V. Tools, Environments

V-a. Github

• Github getting started: https://docs.github.com/en/get-started/onboarding/getting-started-with-your-github-account
• Beginner video, Git and GitHub: https://www.youtube.com/watch?v=RGOj5yH7evk

V-b. Markdown formatting

V-c. Ubuntu Shell

• Unix/Linux Command Reference https://files.fosswire.com/2007/08/fwunixref.pdf
  • Added by Brianna Guarino, github user Brirrito

V-d. Anaconda Environment

V-e. IDE

• Jupyter Notebook and Lab
• Spyder
• VS Code
• PyCharm

V-f. Cloud Servers

• A. Google Colab - free, GPU and TPU engines available

• B. Amazon AWS - about 90 cents/hr for GPU xlarge, free non-GPU available

• C. Binder - free, limit 200 concurrent users, slow with many users.

VI. Miscellaneous

• xkcd comics on programming

• reddit - programming thread

• HackerRank - simple coding practice, many languages

• CodeWars - simple coding practice, many languages

• StackOverflow - answering newbie questions is very good for firming up one's own learning. It's also a good practice for debugging and reading other people's code.

• Cracking the Coding Interview book - uses Java.
  Worked Python solutions available from others.

• Algoexpert.com - about $80-100 for 1 year, look for sales.

• Genius Makers, by Cade Metz, c 2021, Dutton publisher.

  • Has interesting story about the early pioneers of deep learning and the story of AlphaGo AI development.
  • https://www.amazon.com/Genius-Makers-Mavericks-Brought-Facebook/dp/1524742678/ref=asc_df_1524742678/?tag=hyprod-20&linkCode=df0&hvadid=459709175715&hvpos=&hvnetw=g&hvrand=11860445820015786195&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9007587&hvtargid=pla-944267914281&psc=1
  • Added by Jennifer Yoon, github: JennEYoon, #hacktoberfest-2021