ML_projects

Some of my own projects based on exercises from the textbook "Hands on Machine Learning with Scikit-Learn and Tensorflow"

Plotting decision regions

Experimenting with various support vector classification hyperparameters on several different toy datasets, and visualizing classification decision regions with the mlxtend library

Decision trees

An implementation of a random forest from building up individual decision trees

Dimensionality reduction

Illustrating the steps of Principal Component Analysis and other dimensionality reduction approaches using the scikit-learn package for manifold learning. Visualizing some manifold learning algorithms like t-SNE, Isomap, and MDS applied to the MNIST dataset

Kernel Trick

Attempting to explain and illustrate kernel methods in non-linear support vector machines

Ensemble methods/Random Forest

Applying some ensemble methods like voting classifiers, bagging/pasting, AdaBoost, and gradient boosting and trying to visualize the collective decision regions on sample datasets

Spectral clustering/embedding

Some background on laplacian eigenmaps and spectral clustering/embedding