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Coding background

Using assert for testing
Arrays as images, images as arrays
Where and argmin
Where in 2D
2D arrays, images, optimization
Using the pathlib module
Dictionaries
Pandas and dictionaries
Lambda functions

Reading formulae

Vectors and dot products
Numpy, arrays, and vectors
Some algebra with summation
Notation for linear regression models
Vector projection
Introducing principal component analysis
Dimension reduction with Principal Component Analysis

Intermediate regression

Linear regression notation for models with multiple predictors
Attack or defense? An example of multiple regression
Categorical predictors in linear regression
Statistical Adjustment in Multi-predictor Linear Regression Models
Multiple Logistic Regression, Model Selection and Cross-validation
Fitting models with different cost functions
Expressing the linear model with matrices

Classification

Naive Bayes classifiers
k-means clustering
Kernel density estimation

Working with data

The problem with data

The tools

Installing on your computer
The joys and sorrows of notebooks

Repository
Suggest edit
Open issue

.md

The problem with data

The problem with data#

Working with data, working with notebooks.
Keeping track of your analyses
Sharing analyses
Avoiding error

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Kernel density estimation

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Installing on your computer

By Matthew Brett and Peter Rush

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