Image 1 of 1: ‘An infographic showing some of the relationships between AI, ML, and DL’
The image above is by Tukijaaliwa, CC BY-SA 4.0, via Wikimedia Commons,
original source
Image 1 of 1: ‘Types of Machine Learning’
Figure from the Python Data
Science Handbook
Image 1 of 1: ‘Types of Machine Learning’
Image from Vasily
Zubarev via their blog with modifications in blue to denote lesson
content.
Image 1 of 1: ‘Example of linear and polynomial regressions’
Image 1 of 1: ‘Artwork by @allison_horst’
Image 1 of 1: ‘Artwork by @allison_horst’
The physical attributes measured are flipper length, beak length,
beak width, body mass, and sex. 
Image 1 of 1: ‘Comparison of the regressions of our dataset’
Image 1 of 1: ‘Comparison of the regressions of our dataset’
Image 1 of 1: ‘Comparison of the regressions of our dataset’
Image 1 of 1: ‘Comparison of the regressions of our dataset’
Image 1 of 1: ‘Visualising the penguins dataset’
Image 1 of 1: ‘Visualising the penguins dataset’
Image 1 of 1: ‘Decision tree for classifying penguins’
Image 1 of 1: ‘Decision tree for classifying penguins’
Image 1 of 1: ‘Classification space for our decision tree’
Image 1 of 1: ‘Performance of decision trees of various depths’
Image 1 of 1: ‘Simplified decision tree’
Image 1 of 1: ‘Classification space of the simplified decision tree’
Image 1 of 1: ‘Classification space generated by the SVM model’
Image 1 of 1: ‘Stacking’
Image 1 of 1: ‘Stacking’
Image 1 of 1: ‘Stacking’
Image 1 of 1: ‘Random Forests’
Image 1 of 1: ‘random forest trees’
Image 1 of 1: ‘random forest clf space’
Image 1 of 1: ‘Regressor predictions and average from stack’
Image 1 of 1: ‘Plot of the random clusters’
Image 1 of 1: ‘Plot of the fitted random clusters’
Image 1 of 1: ‘An example of kmeans failing on non-linear cluster boundaries’
Image 1 of 1: ‘Kmeans attempting to classify overlapping clusters’
Increasing n_samples to 4000 and cluster_std to 3.0 looks like this:
The straight
line boundaries between clusters look a bit strange.
Image 1 of 1: ‘Spectral clustering on two concentric circles’
Image 1 of 1: ‘Spectral clustering viewed with an extra dimension’
Image 1 of 2: ‘Kmeans attempting to cluster the concentric circles’
Image 2 of 2: ‘Spectral clustering on the concentric circles’
Image 1 of 1: ‘MNIST example illustrating all the classes in the dataset’
Image 1 of 1: ‘MNIST example of a single image’
Image 1 of 1: ‘SKLearn image with highlighted pixels’
Image 1 of 1: ‘SKLearn image with highlighted pixels’
Image 1 of 1: ‘Reduction using PCA’
Image 1 of 1: ‘Reduction using PCA’
Image 1 of 1: ‘Reduction using PCA’
Image 1 of 1: ‘Reduction using PCA’
Image 1 of 2: ‘Reduction using PCA’
Image 2 of 2: ‘Reduction using PCA’
Image 1 of 1: ‘Reduction to 3 components using pca’
Image 1 of 1: ‘Reduction to 3 components using tsne’
Image 1 of 1: ‘A diagram of a perceptron’
Image 1 of 1: ‘A multi-layer perceptron’
