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

Similarity or dissimilarity measures are core components of clustering algorithms that cluster similar data points into the same clusters. In contrast, dissimilar or distant data points are placed into different clusters. Although the choice of a similarity/dissimilarity measure is task-dependent, it's good to know the common ones.

> **Note:** The measures involve two data points, say $\bold x$ and $\bold y$, in $\R^d$.

# Minkowski distance
The **Minkowski distance** $d_{mink}$ between points $\bold x$ and $\bold y$ is defined as follows:

$$
d_{mink}(\bold x, \bold y, p)=\bigg(\sum_{i=1}^d|x_i - y_i|^p \bigg)^\frac{1}{p}
$$

Here, $p \in \Z^+$, that is, $p$ is a positive integer. The code below implements Minkowski distance given two points `x` and `y` for a given value of `p`:

import numpy as np

def Minkowski_distance(x, y, p=2):
  return np.sum(np.abs(x-y)**p)**(1./p)

d, p = 20, 3
x, y = np.random.rand(d), np.random.rand(d)

print(f'The Minkowski distance between x and y is {Minkowski_distance(x, y, p=2)}')

## The p-norm
The **p-norm** of a vector $\bold x \in \R^d$, denoted by $\|\bold x \|_p$, is defined as $\big(\sum_{i=1}^d|x_i|^p \big)^\frac{1}{p}$, where $p \in \Z^+$.

> **Note:** Minkowski distance between points $\bold x$ and $\bold y$ for a given $p$ is the p-norm of the vector $\bold x - \bold y$. 
$$d_{mink}(\bold x, \bold y, p)=\|\bold x-\bold y\|_p$$

# Euclidean distance
**Euclidean distance** is a special case of Minkowski distance. In particular, the Euclidean distance between the points $\bold x$ and $\bold y$, denoted by $d_{eucl}$, is the Minkowski distance for $p=2$.

$$
d_{eucl}(\bold x, \bold y)=d_{mink}(\bold x, \bold y, 2)=\|\bold x-\bold y\|_2
$$

Similarity and Dissimilarity Measures

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

Similarity and Dissimilarity Measures

Minkowski distance