Gain insights into machine learning concepts, implement and compare algorithms with scikit-learn, and explore supervised learning, clustering, regression, SVMs, autoencoders, and ensemble learning with practical projects using Python.

ml.tar.gz

Playground

Playground-SPA

Complexity CH-1

regulizer CH-1

regulizer CH-1-S

regulizer ch1_v2

complexity ch1_v2

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ST_kernel_trick-LIVE

python

pytorch

ST_Autoencoders

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SPA_tqdm

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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

**Soft-margin SVM** is a type of SVM algorithm that allows for some misclassification of data points to create a more flexible decision boundary. It can handle cases where the data isn't perfectly separable by a linear boundary. By allowing some misclassification, the algorithm can find a decision boundary that captures the general trend in the data while avoiding overfitting.

# What is soft-margin SVM?
Finding a mapping $\phi$ that allows for linear separation with a good margin might prove difficult for certain datasets. Some datasets might not even be capable of linear separation at all. To tackle such situations, it becomes necessary to relax the condition of linear separability with a margin of at least $1$. Our approach involves allowing certain points to violate the hard-margin constraint, but we aim to minimize the total violation as much as possible. This technique is commonly referred to as soft-margin SVM.

# Hard-margin vs. soft-margin
Hard-margin and soft-margin SVM differ in how they handle non-linearly separable data. Hard-margin SVM looks for a decision boundary that perfectly separates the classes with the largest possible margin, while soft-margin SVM allows for some misclassification with slack variables.

Learn what the soft-margin SVM algorithm is and how to implement it using the CVXPY library.

Soft-Margin SVM

Course Overview

Supervised 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

Soft-Margin SVM

What is soft-margin SVM?

Hard-margin vs. soft-margin