Implement k-nearest neighbors.

Build a Binary Classification function in 2D.

Fit a linear model with mean squared error.

Implement gradient descent.

Implement Convolution.

Tabulate confusion matrix and caclulate accuracy, precision and recall.

Compute descriptive statistics of times series, interpolation, and smoothing.

Compute median in time series data

Compute precision, recall, f1 score

Evaluate ML models

Evaluate if data come from the same distribution

Quantify if there is a change in the stock price

Implement cumulative distribution function

Implement how to deal with time series data

Visualize data features and quantify their relations and diagnose linear regression

Detect when deployment change has happened from binary time series data.

Average Performance per Model per Object