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Introduction to Machine Learning
Houston Muzamhindo
Course topics and videos

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1. Introduction to Machine Learning
Introduction to Machine Learning
2. Introduction to Statistical Learning
Modelling for Prediction versus Modelling for Inference
Parametric versus Non-Parametric Methods
Trade-off Between Model Accuracy and Model Interpretability
Supervised versus Unsupervised Learning
Regression versus Classification
Assessing Model Accuracy - Measure of Fit
Bias-Variance Trade-Off
Assessing Model Fit - Classification Setting
Classification Example - K-Nearest Neighbours (kNN)
Confidence Intervals for Coefficient Estimates for Simple Linear Regression Models
Hypothesis Test of Coefficient Estimates for Simple Linear Regression
Accuracy of Coefficient Estimates for Simple Linear Regression
Estimating Simple Linear Regression's Model Coefficients
3. Linear Regression
Introduction to Linear Regression
Accessing Simple Linear Regression Model Accuracy
Residual Standard Error (RSE)
R-Squared Statistic
Multiple Linear Regression
Estimating Multiple Linear Regression Coefficients
Dealing with Qualitative Variables
Including Interaction Terms in the Model (Non-Additive Models)
Including Non-linear Terms in the Model
Problem #1 - Non-linearity of the data
Problem #2 - Correlation of Error Terms
Problem #3 - Non-constant variance of error terms
Problem #4 - Outliers
Problem #5 - High leverage points
Problem #6 - Collinearity
4. Classification
5. Linear Model Selection and Regularization
6. Moving Beyond Linearity
7. Tree Based Methods
8. Support Vector Machines

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