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

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

# Curse of dimensionality
**Curse of dimensionality** in machine learning refers to the challenges and computational complexities that arise when dealing with a large number of features (high-dimensional data/high-dimensional feature space). As the number of features or dimensions increases, the amount of data needed to maintain reliable and meaningful patterns also increases, often leading to increased data and computational demands and the risk of overfitting.

## Example
Consider a product recommendation system where each product is described by multiple features such as price, size, color, brand, and so on. As the number of features increases, possible combinations grow exponentially, making it harder to find meaningful relationships between products and user preferences. This high-dimensional data can lead to sparse data points, which makes accurate predictions more challenging and requires more data to avoid unreliable results, hence, illustrating the curse of dimensionality.



Gain insights into PCA theory, address dimensionality challenges, and acquire hands-on skills in PCA implementation using real-world data.

Principal Component Analysis

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

Principal Component Analysis

Curse of dimensionality

Example