Data Science Learning

==The purpose of this page is to keep track of various learning resources, a learning plan and progress tracker==

• [[Data Science Cheat Sheet]]

Personal Projects

• [[aaronson-oracle-baseball]]

Progress Tracker

On-going

• [[Coursera - Machine Learning Specialization]]

Planned

• [[fast-ai]]
• [[Coursera - Deep Learning Specialization]]

Finished

• [[Hands On Machine Learning]]

Resources

Useful links

• https://awesomedataengineering.com/
• https://a16z.com/2020/10/15/the-emerging-architectures-for-modern-data-infrastructure/
• https://www.dynamicyield.com/lesson/introduction-to-ab-testing/

Courses

• [[fast-ai]]
  • A top down approach to teaching you advanced ML, should be done in conjunction with Andrew's [[Deep Learning Specialization]] course
  • 7/10
• [[Big Data Specialization Degree]]
  • 4 full courses, will require a lot of commitment
  • 6/10
• [[Coursera - Machine Learning Specialization]]
  • Andrew Ng's introduction to ML course
  • Source: https://www.coursera.org/learn/machine-learning#syllabus
  • I have completed the first 8 weeks of this, I never did the large scaled projects at the end
  • 8/10
• [[Coursera - Deep Learning Specialization]]
  • Advanced Andrew Ng's course on neural networks, picks up after his intro ML class [[Machine Learning]]
  • How does this differ from [[fast-ai]]?
    • fast-ai works from top-down while this course goes from bottom-up
    • fast-ai teaches the art of driving, while deep learning ai teaches you the engineering behind the car
  • 8/10
• [[Data Science A-Z]] #course
  • Paid on Udemy, ranked highly
  • 21 hours of content, emphasizes on concepts instead of programming application because it is taught in gretl, tableau and excel instead of R or python
  • 9/10
• [[Applied Data Science with Python Specialization]] #course
  • 5 courses, requires large dedication
  • Focused on teaching tools like pandas, scikit-learn, nltk, networkx to gain insights to data
  • 8/10
• [[Fast Deep RL]]
  • https://courses.dibya.online/p/fastdeeprl
• [[Hugging Face Course]] #course
  • Focused on natural language processing using libraries from hugging face
  • A pretty useful course to get a handle on the current most used libraries
  • https://huggingface.co/learn/nlp-course/chapter1/1

Books

• [[Elements of Statistical Learning]] #books

  • Dense but worth it - Keep reading chapters 1-4 and 7-8 until you understand it

  • Source: https://web.stanford.edu/~hastie/ElemStatLearn//printings/ESLII_print10.pdf

  • https://www.reddit.com/r/MachineLearning/comments/5z8110/d_a_super_harsh_guide_to_machine_learning/

  • 7/10

• [[Deep Learning]] #books
  • Source: https://www.deeplearningbook.org/front_matter.pdf
  • Heralded as the bible of machine learning, but it might be too rigorous to be useful for me at this moment
  • 3/10
• [[Hands On Machine Learning]]
  • O'reilly book with notebooks on Github, pretty good reviews
  • Good for hands on machine learning techniques, it has lots of project based teaching
  • Part I focus on machine learning techniques, Part II focus on neural networks with [[TensorFlow]]
  • 9/10
• [[Neural Networks and Deep Learning]] #books
  • Source: http://neuralnetworksanddeeplearning.com/about.html
  • Focused on the concepts of neural networks
  • Not project oriented
  • 2/10
• [[Introduction to Algorithms]] #books
  • Focused on algorithms
  • 8/10
  • Source: https://www.amazon.com/gp/product/0262033844/ref=as_li_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=0262033844&linkCode=as2&tag=adilkhash-20&linkId=74742875db503b1a899ca35159749067
• [[Bayesian Probabilities for Hackers]] #books
  • https://www.amazon.com/Bayesian-Methods-Hackers-Probabilistic-Addison-Wesley/dp/0133902838
  • https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
  • A fast tracked book on Bayesian Probabilities without delving into too much math
  • Using Python and PyMC
• From Python to Numpy
  • https://www.labri.fr/perso/nrougier/from-python-to-numpy/