A list of 2021 Resources - pt. 1

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?🤔