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Machine Learning Maths and Modelling
1. Introduction
1.0 Mathematics, Machine Learning, and Python
1.1 Getting Started
1.2 Working In The Sandbox
2. Framing the Problem
2.0 Probability and Statistics
2.1 Statistics and Machine Learning
2.3 Random Variable and Bayes’ Theorem
2.4 Common Data Fallacies
2.5 Problem Categorisation
2.6 Checkpoint
2.7 Discussion Points
3. Data Preparation
3.0 Working With Data Frames
3.1 Histograms
3.2 Scatterplots
3.3 Time Series
3.4 Box Plots
3.5 Heat Maps
3.6 Data Wrangling
3.7 Checkpoint
3.8 Discussion Points
4. Machine Learning Models
4.0 Regression
4.1 Classification - Non-parametric Models
4.2 Classification - Random Forests
4.3 Clustering
4.4 Dimensionality Reduction
4.5 Checkpoint
4.6 Discussion Points
5. Machine Learning Process
5.0 Hypothesis Development
5.1 Selecting Model Types
5.2 Fitting, Bias and Variance
5.3 Cross Validation
5.4 Comparing Models and Performance
5.5 Bootstrap
5.6 Checkpoint
5.7 Discussion Points
6. Conclusion
6.0 What Did You Learn?
4.1 Classification - Non-parametric Models
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