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

# An overview of three clustering algorithms
Clustering is an unsupervised learning technique that divides the dataset into distinct groups such that the points within each cluster are more similar to each other than they are to points in other clusters. There are many different clustering algorithms, and each has its own strengths and weaknesses. Here's a comparison of three popular clustering algorithms: $k$-means, DBSCAN, and agglomerative clustering.

## $K$-means
This is a centroid-based algorithm or a distance-based algorithm, where we calculate the distances to assign a point to a cluster. $K$-means starts by randomly selecting $k$ initial centroids and then assigns each data point to the cluster corresponding to the nearest centroid. The centroids are then updated to the mean of the points in the corresponding cluster, and the process is repeated until convergence. One of the main advantages of $k$-means is that it's computationally efficient and easy to implement. However, it can get stuck in local optima, and it's sensitive to the initial centroids chosen. Additionally, it assumes that the clusters are spherical and equally sized, which might not always be the case.

### Example
Let's try a scenario where $k$-means performs better as compared to DBSCAN and agglomerative clustering:

Learn about the comparison of three famous clustering algorithms.

Clustering Algorithms Comparison

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

Clustering Algorithms Comparison

An overview of three clustering algorithms

$K$ -means

Detect Cyber Intrusion Using Machine Learning

Project: Bag of Visual Words

Face Recognition Using Kernel Linear Discriminant

Early Stage Diabetes Prediction Using Ensemble Learning

Image Reconstruction Using PCA

Image Colorization using Autoencoders

Colorful Face Generation with VAEs

Clustering Algorithms Comparison

An overview of three clustering algorithms

KKK-means

$K$ -means