Linear Regression by gradient descent
This lab will cover:
Part 1: Read and parse the initial dataset
Part 2: Create and evaluate a baseline model
Part 3: Train (via gradient descent) and evaluate a linear regression model
Part 4: Train using MLlib and tune hyperparameters via grid search
Part 5: Add interactions between features
This lab will cover:
Part 1: Featurize categorical data using one-hot-encoding (OHE)
Part 2: Construct an OHE dictionary
Part 3: Parse CTR data and generate OHE features
Part 4: CTR prediction and logloss evaluation: (ROC)
Part 5: Reduce feature dimension via feature hashing: Hyperparameter heat map
This lab will cover:
Part 1: Work through the steps of PCA on a sample dataset
Visualization 1: Two-dimensional Gaussians
Part 2: Write a PCA function and evaluate PCA on sample datasets
Visualization 2: PCA projection
Visualization 3: Three-dimensional data
Visualization 4: 2D representation of 3D data
Part 3: Parse, inspect, and preprocess neuroscience data then perform PCA
Visualization 5: Pixel intensity
Visualization 6: Normalized data
Visualization 7: Top two components as images
Visualization 8: Top two components as one image
Part 4: Perform feature-based aggregation followed by PCA¶
Visualization 9: Top two components by time
Visualization 10: Top two components by direction