A list of 2021 Resources - pt. 1

in #bloglast year (edited)

Do you guys/girls also bookmark lots of resources to read after? Well I am actually one for those who reads them 😂 I wanted to share some of those resources from this year, you might also find them useful:

  1. 10 Must-Read Machine Learning Books
  2. Medicine's Machine Learning Problem
  3. AlphaSignal
  4. Neural Netoworks and Deep Learning
  5. Data Mining and Machine Learning: Fundamental Concepts and Algorithms
  6. Deep Learning Book
  7. How I Prepared for the TensorFlow Developer Certification
  8. Classifying emotions using audio recordings in Python
  9. How to download and visualize your Twitter network
  10. 17 types of similarity and dissimilarity measures used in data science
  11. New Courses: Machine Learning Engineering for Production
  12. What is Data Extraction? A Python Guide to Real-World Datasets
  13. How to build an AutoML app in Python
  14. How to Download High-Resolution Satellite Data for Anywhere on Earth
  15. Why You Shoudn't Hire More Data Scientist
  16. How To Deploy Machine Learning Models
  17. The Last Mile in Shipping Data Science Projects Well
  18. Exploratory Data Analysis, Visualization, and Prediction Model in Python
  19. Data Science, Meaning, and Diversity
  20. 5 Data Science Open-source Projects You Should Consider Contributing to
  21. Lifecycle of an ML Project
  22. Network Analysis
  23. Neural network from TENET exploiting time inversion
  24. Social Network Analysis: From Graph Theory to Applications with Python
  25. Deploying An ML Model With FastAPI - A Succint Guide
  26. MLOps Best Practices for Data Scientists
  27. How do I know which graph to use?
  28. Semi-Automated Exploratory Data Analysis (EDA) in Python
  29. How to deploy Machine Learning models as a Microservice using FastAPI
  30. How to use Plotly.js in React to Visualize and Interact with Your Data
  31. Understanding Bias and Fairness in AI Systems
  32. What do you make of this?
  33. Data Science. The Central Limit Theorem and sampling
  34. Automating Machine Learning tasks using EvalML Libariry
  35. To Learn Data Science Faster, Teach It
  36. Announcing Power BI in Jupyter
  37. Awesome community detection
  38. Does MLOps Live Upto The Hype?
  39. MLOps Basics
  40. Google colab notebooks are already running Deepmind's AlphaFOld v.2
  41. Not another Qlik Sense Spotify App
  42. Creating a Modern, Open Source MLOps Stack at Home

Can you guess what I've been working on based on this?🤔

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Thank you for these resources, you may be interested to save them in the decentralized collective research tool https://www.publicdomain.live/

Nice, thanks for this. Always sharing the best resources from your side!