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

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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

# Nonlinear dimensionality reduction
Datasets might not always conform to a linear subspace. In such cases, employing linear techniques like PCA for dimensionality reduction proves ineffective. To address this, nonlinear dimensionality reduction techniques come into play. In this approach, data points undergo encoding/transforming via a nonlinear function. Let's consider a scenario with $n$ data points existing in a $d$-dimensional space, organized as columns in a matrix denoted as $X_{d \times n}$. The aim is to derive corresponding $k$-dimensional nonlinear encodings (latent space), denoted as $Z_{k \times n}$, achieved through the following process:

$$
Z = f_{W_e}(X)
$$ 

Here, $f$ represents a nonlinear parametric function, often referred to as an encoder, driven by parameters $W_e$. If the goal is to encode in a manner that permits successful decoding or reconstruction of the original data, a nonlinear function $g_{W_d}$, known as a decoder, can be formulated as follows:

$$
\hat{X} = g_{W_d}(Z)
$$

Minimization of the squared loss $|X-\hat{X}|^2$ is carried out to estimate the parameters for both the encoder and decoder. This process facilitates learning representations that encapsulate the salient features of the data in a manner that transcends the limitations of linear approaches like PCA.

> **Note:**
>* It's a prevalent practice to represent both the encoder and decoder as neural networks, often referred to as encoder-decoder networks.
>
>* When the intention of the encoder-decoder network is to achieve dimensionality reduction, the network is commonly referred to as an autoencoder.

**Autoencoders** are a specific type of neural network architecture used for unsupervised learning tasks. They're designed to take high-dimensional input data, encode it into a lower-dimensional representation, and then decode that representation back into the original format. This allows autoencoders to be used for tasks such as dimensionality reduction and feature extraction.


Explore the world of autoencoders, from uncovering the significance of encoders and decoders to understanding their architecture and applications.

Autoencoders

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

Autoencoders

Nonlinear dimensionality reduction