Machine Learning
This page contains links to html renders of Jupyter Notebooks that I developed in Spring 2019 for the course DSCI 35600 - Machine Learning offered at Lindenwood University.
Disclaimer: These notebooks are not intended to be a standalone resource for machine learning. These were created to supplement lecture presentations. Many of the lessons (especially the later ones) would benefit from additional exposition.
Lecture Notebooks
- 01 - Introduction to Machine Learning
- 02 - Overview of Supervised Learning
- 03 - Linear Regression
- 04 - Testing LinReg Class
- 05 - Polynomial Regression
- 06 - Encoding Categorical Variables
- 07 - Feature Scaling
- 08 - L1 and L2 Regularization
- 09 - Classification Metrics
- 10 - Logistic Regression
- 11 - KNN Classifier
- 12 - Titanic Dataset
- 13 - Decision Tree Classifier
- 14 - Decision Tree - Titanic
- 15 - Voting Classifiers
- 16 - Voting Classifier - Titanic
- 17 - Random Forests
- 18 - Support Vector Machines - Part 1
- 19 - Support Vector Machines - Part 2
- 20 - Support Vector Machines - Part 3
- 21 - Decision Tree Structure
- 22 - Cross Validation
- 23 - Grid Search
- 24 - Principal Component Analysis
- 25 - PCA for Facial Recognition
- 26 - PCA Interpretation
- 27 - K-Means Clustering
- 28 - K-Means for Image Compression
- 29 - The Artificial Neuron
- 30 - Introduction to Neural Networks
- 31 - MNIST Dataset
Other Resources The textbooks that I used in this course were: