Fundamentals of Machine Learning: A Pythonic Introduction/

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Regression

Many real-world problems require us to predict a number, not a category. Whether we want to estimate the price of a house, forecast tomorrow’s temperature, or predict how much a customer will spend, the output is a continuous numerical value.

These kinds of tasks fall under regression, a core technique in supervised learning.

Regression

Regression is a technique that creates a mapping function ( $f$ ) to approximate the relationship between a real number (a continuous value) as a target label and its input features.

In other words, given a dataset: $D=\{(\mathbf{x}_1, y_1), (\mathbf{x}_2, y_2),...,(\mathbf{x}_n, y_n)\}$ , where $\mathbf{x}_i$ is the input and $y_i$ is the actual target, the regression function will be:

\mathbf{\hat{y}}_i = f(\mathbf{x}_i) \;\;\;\;\;\; \forall i

$\mathbf{\hat{y}}_i$

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Course Overview

Supervised Learning

Detect Cyber Intrusion Using Machine Learning

Clustering

Project: Bag of Visual Words

Generalized Linear Regression

Face Recognition Using Kernel Linear Discriminant

Support Vector Machine

Logistic Regression

Ensemble Learning

Early Stage Diabetes Prediction Using Ensemble Learning

Decoding Dimensions: PCA and Autoencoders

Image Reconstruction Using PCA

Image Colorization using Autoencoders

Colorful Face Generation with VAEs

Appendix

Wrapping Up

How to Predict the Traffic Volume Using Machine Learning

Regression

Regression