100 Days of Code in Python

I have started 100 days of code to document my coding journey and stay consistent also for accountability.

I am doing Machine Learning starting from Scratch

Daily Tasks

1. Day - 0 Numpy 🔗
2. Day - 1 Numpy, Pandas 🔗
3. Day - 2 Pandas, Data Analysis project 🔗
4. Day - 3 Pandas (Grouping & Aggregating Data 🔗
5. Day - 4 Pandas (Cleaning data & Time Series Analysis) 🔗
6. Day - 5 Pandas (Working with different data formats) & Data Analysis project 🔗
7. Day - 6 Linear Algebra (Vectors Basics) & Data Analysis project 🔗
8. Day - 7 Matplotlib library 🔗
9. Day - 8 Matplotlib Library 🔗
10. Day - 9 Linear Algebra (Matrices, Linear Transformations) & Data Analysis project 🔗
11. Day - 10 Matplotlib & Data analysis on Titanic Dataset 🔗
12. Day - 11 Data analysis on Titanic Dataset 🔗
13. Day - 12 Data analysis on Titanic Dataset 🔗
14. Day - 13 Data analysis on Titanic Dataset 🔗
15. Day - 14 Matplotlib Library 🔗
16. Day - 15 Matplotlib Library🔗
17. Day - 16 Matplotlib & Seaborn Library 🔗
18. Day - 17 Seaborn Library & Data Analysis project 🔗
19. Day - 18 Seaborn Library & Data Analysis project 🔗
20. Day - 19 Intro to Machine Learning & House price prediction 🔗
21. Day - 20 Intermediate Machine Learning & House price prediction 🔗
22. Day - 21 Intermediate Machine Learning & House price prediction 🔗
23. Day - 22 Linear Algebra & House price prediction 🔗
24. Day - 23 Statistics & House price prediction 🔗
25. Day - 24 Linear Algebra & Statistics 🔗
26. Day - 25 Feature Engineering & House price preidction 🔗
27. Day - 26 Feature Engineering 🔗
28. Day - 27 Feature Engineering & House price prediction 🔗
29. Day - 28 Linear Algebra & PCA 🔗
30. Day - 29 Linear Algebra & Statistics 🔗
31. Day - 30 Titanic dataset prediction & GHW hackathon 🔗
32. Day - 31 Titanic dataset prediction & GHW hackathon 🔗
33. Day - 32 Titanic dataset prediction & GHW hackathon 🔗
34. Day - 33 Titanic dataset prediction & GHW hackathon 🔗
35. Day - 34 Titanic dataset prediction & GHW hackathon 🔗
36. Day - 35 Regression & Classification Random Forest 🔗
37. Day - 36 Started ML Speicalization course & Revised Mathematics 🔗
38. Day - 37 Supervised vs unsupervised learning and Regression model 🔗
39. Day - 38 Linear Regression and Notations in ML 🔗
40. Day - 39 Linear Regression and it's Cost function 🔗
41. Day - 40 Working of Cost function 🔗
42. Day - 41 Gradient Descent 🔗
43. Day - 42 Completed Week 1 of ML course & Gradient Descent 🔗
44. Day - 43 Linear regression with mutliple variables 🔗
45. Day - 44 Vectorization and Gradient Descent 🔗
46. Day - 45 Feature Scaling 🔗
47. Day - 46 Gradient Descent and Simple Linear Regression code 🔗
48. Day - 47 Feature Engineering and Polynomial Regression 🔗
49. Day - 48 (Week 2 assignment) Linear Regression with Gradient Descent code 🔗
50. Day - 49 Linear Regression code in Python 🔗
51. Day - 50 Logistic Regression and Sigmoid function 🔗