Machine Learning with R

Part A - introduction

• A1 - Laying the foundations in R (Browser | pdf | rcode)

Part B - lasso and ridge regression

• B1 - A small recap on linear regression (Browser | pdf | rcode)
• B2 - Using regularization to prevent overfitting and perform feature selection (Browser | pdf | rcode)

Part C - Supervised Learning: Bagging and Boosting, tree-methods

• C1 - Supervised learning: tress and bagging (Browser | pdf | rcode)

• C2 - Supervised learning: random forests (Browser | pdf | rcode)

• C3 - Supervised learning: gradient boosting (Browser | pdf | rcode)

Part D - Supervised Learning: Neural Network

• D - Supervised learning: neural network (Browser | pdf | rcode)

Part E - Unsupervised Learning: kmeans, hdbscan

• E - Unsupervised Learning: kmeans, hdbscan

Part F - The dangers of machine learning

• F - The dangers of machine learning

Part G - reticulate package: Umap

• G - reticulate package: Umap