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