Learn machine learning with scikit-learn, covering supervised learning, clustering, regression, SVMs, autoencoders, and ensemble methods through practical Python projects.

ml.tar.gz

Playground

Playground-SPA

Complexity CH-1

regulizer CH-1

regulizer CH-1-S

regulizer ch1_v2

complexity ch1_v2

code_widget

ST_kernel_trick-LIVE

python

pytorch

ST_Autoencoders

code_widget_torchsummary

code_widget_tqdm

SPA_tqdm

AE_multiple

SPA_server

Implementation of NN

c1l3

Implementation of NN-copy

This course focuses on core concepts, algorithms, and machine learning techniques. It explores the fundamentals, implements algorithms from scratch, and compares the results with scikit-learn, the Python machine learning library. This course contains examples, theoretical knowledge, and codes for various ML algorithms.

You’ll start by learning the essentials of machine learning and its applications. Then, you’ll learn about supervised learning, clustering, and constructing a bag of visual words project, followed by generalized linear regression, support vector machines, logistic regression, ensemble learning, and principal component analysis. You’ll also learn about autoencoders and variational autoencoders and end with three exciting projects.

By the end, you’ll have a solid understanding of machine learning and its algorithms, hands-on experience implementing such algorithms and applying them to different problems, and an understanding of how each algorithm works with the provided examples.

Fundamentals of Machine Learning: A Pythonic Introduction

**Discriminant analysis** is a statistical technique used to classify objects or cases into two or more groups based on a set of predictor variables. The aim of discriminant analysis is to find a function or set of functions that can discriminate between the groups with high accuracy.


# Discriminant function
Consider a $k$-class classification dataset $D=\{(\bold x_1, y_1), (\bold x_2, y_2), \dots,(\bold x_n, y_n)\}$, where $\bold x_i \in \R^d$ and $y_i \in \{1, 2, \dots, k\}$. A **discriminant function** assigns an input vector $\bold x$ to one of the $k$ classes.

## Generalized linear discriminant function
A **generalized linear discriminant** is a discriminant function that’s linear in the transformed features $\phi(\bold x)$.

### Two-class formulation
When the number of classes is $2$, that is, $k=2$, the generalised linear discriminant can be formulated as a generalised linear regression model along with a threshold. In particular, considering the class labels from the set $\{-1,1\}$, the generalised linear discriminant can be defined as follows:

$$
\hat y_i=g(\bold w^T\phi(\bold x_i))
$$

where:
$$
\begin{cases} 
      g(\bold w^T\phi(\bold x_i))=1 & \bold w^T\phi(\bold x_i)>0 \\
      g(\bold w^T\phi(\bold x_i))=-1 & \bold w^T\phi(\bold x_i)<0
   \end{cases}
$$

The decision boundary, also known as the classification boundary, can be defined by the equation $\bold w^T\phi(\bold x_i)=0$, which represents a hyperplane (and therefore is linear) in the feature space.



Discover kernel linear discriminant for multiclass classification with two-class and multiclass formulation.

Kernel Linear Discriminant

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

Kernel Linear Discriminant