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

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ST_kernel_trick-LIVE

python

pytorch

ST_Autoencoders

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Implementation of NN

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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

# Generalized linear model for regression
A regression model that is linear in parameters $\bold w$, and might not necessarily be linear in the input features $\bold x$, is known as a **generalized linear model for regression**.

$$f_\bold{w}(\bold x)=\bold w^T\phi(\bold x)$$

> **Note:** A generalized linear model is linear in transformed features $\phi(\bold x)$, and is typically nonlinear in the input features $\bold x$. 


## Nonlinear transformations in regression
Suppose we want to predict the total marks a student will obtain in an exam based on the number of hours they studied and the number of times they attended class. We collected data on 20 students and recorded the number of hours they studied, the number of times they attended class, and their obtained marks.

To build a linear regression model, we can start with a simple linear equation of the form:

$$y = w_0 + w_1x_1 + w_2x_2$$



Here, $y$ is the obtained marks, $x_1$ is the number of hours studied, and $x_2$ is the number of times attended class.

However, we might find that the relationship between the input features and the marks obtained isn’t quite linear. For example, the effect of the number of times attended class on obtained marks might depend on the number of hours studied. In this case, we can use a generalized linear model for regression.

One way to do this is to transform the input features using a set of basis functions $\phi(x)$. For example, we can use the following basis functions:

$$\phi_1(x) = x$$
$$\phi_2(x) = \sqrt{x}$$

Then, we can use a linear model in the transformed feature space:

$$f_\bold{w}(\bold x) = w_0 + w_1\phi_1(x_1) + w_2\phi_2(x_1) + w_3\phi_1(x_2) + w_4\phi_2(x_2)$$

Note that this model is linear in the parameters $w_i$, but might be nonlinear in the input features $x_i$ due to the nonlinear basis functions.

Using this model, we can fit the parameters $w_i$ to the training data using a method such as least squares regression. Then, we can use the model to predict the exam grade of a new student based on the number of hours they studied and the number of times they attended class.


# Generalized linear model for classification
A classification model that has a linear classification boundary in the transformed features $\phi(\bold x)$ is known as a generalized linear model for classification.
>**Note:** A function that separates data points of different classes is referred to as a **classification boundary**. If a classification problem has only two classes, it's known as a **binary classification problem**.


Acquire knowledge on generalized linear models for regression and classification through interactive code.

Generalized Linear Models

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

Generalized Linear Models

Generalized linear model for regression

Nonlinear transformations in regression