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    Machine Learning
    Robbie Beane

    Robbie Beane

    Assistant Professor of Data Science
    Program Director for Computer Science
    Maryville University

    • St. Louis, MO
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    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:

    • An Introduction to Statistical Learning
    • Hands-On Machine Learning with Scikit-Learn and TensorFlow
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