This page contains links to my notes of a series of blog posts during which I attempted to understand Machine Learning. I've collated all the posts to date below:
Section 1 - Kaggle Titanic competition - posts 1 - 5
Topics covered: Classification, pivot tables, Random Forests, features importance, k-fold cross validation, hyper-parameter turning
Section 2 - Kaggle Ames House Price competition - posts 6 - 10
Topics covered: Regression, Random Forests, Jupyter, data cleansing, label encoding, Gradient Boosted Regressor
Section 1 - Kaggle Titanic competition - posts 1 - 5
Topics covered: Classification, pivot tables, Random Forests, features importance, k-fold cross validation, hyper-parameter turning
- Post 1 - An Actuary learns Machine Learning – Part 1 – Kaggle/Titanic/Excel
- Post 2 - An Actuary learns Machine Learning – Part 2 – Spyder/Random Forest/Hyper-Parameters
- Post 3 - An Actuary learns Machine Learning – Part 3 – Automatic testing/feature importance/K-fold cross validation
- Post 4 - An Actuary learns Machine Learning – Part 4 – Error correction/data cleansing/Feature Engineering
- Post 5 - An Actuary learns Machine Learning – Part 5 – lots of machine learning models
Section 2 - Kaggle Ames House Price competition - posts 6 - 10
Topics covered: Regression, Random Forests, Jupyter, data cleansing, label encoding, Gradient Boosted Regressor
- Post 6 - An Actuary learns Machine Learning - Part 6 - Jupyter/Regression/Kaggle house prices
- Post 7 - An Actuary learns Machine Learning - Part 7 - Sub-plots /Null Values/ Random Forests
- Post 8 - An Actuary learns Machine Learning - Part 8 - Data Cleaning / more Null Values / more Random Forests
- Post 9 - An Actuary learns Machine Learning - Part 9 - Cross Validation / Label Encoding / Feature Engineering