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

# Support vectors
Support vectors are the data points from each class that are the closest to the hyperplane. They're responsible for defining the decision boundary or the hyperplane and, ultimately, the data classification. All other data points that aren't support vectors do not affect the decision boundary or the hyperplane and do not contribute to the data classification.

The blue dashed line in the above graph represents the hyperplane, and the black circles with no fill are the support vectors, as these are the closest points to the hyperplane. These support vectors are the most important points in the SVM algorithm because they define the decision boundary and the margin between the two classes. Further, the plot also shows that the support vectors are few in number and lie at the boundary of the two classes. These points are crucial for determining the optimal hyperplane that separates the two classes with maximum margin. Removing or changing these points can alter the hyperplane and the classification decision boundary.
## Identifying support vectors
All the support vectors $(\bold x_i,y_i)$ satisfy the equality constraint $y_i\phi(\bold x_i)^T\bold w^*=1$ given the best hyperplane $\bold w^*$. The following code highlights the support vectors:

Identify support vectors using distance computation and equation constraint.





Identifying Support Vectors

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

Identifying Support Vectors

Support vectors

Identifying support vectors