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

# What is gradient descent?
Gradient descent is an optimization algorithm used for finding the minimum of a function. It's an iterative algorithm that starts from an initial guess of the minimum and then takes steps in the direction of the negative 
#key# gradient: Gradient is the vector of partial derivatives of a function concerning its variables, and it represents the direction and magnitude of the steepest ascent of the function. #key# of the function at that point. The negative gradient of a function points in the direction of the steepest descent, which is the direction in which the function decreases most rapidly. The algorithm gradually converges to the minimum of the function by taking steps toward the negative gradient.

# Gradient descent algorithm
Here are the steps involved in the gradient descent algorithm:

1. Choose an initial guess for the minimum of the function.

2. Calculate the gradient of the function at the current guess. This involves taking the partial derivative of the function with respect to each of the input variables.

3. Multiply the gradient by a small positive number called the learning rate, which determines the step size of the algorithm.

4. Subtract the result from the current guess to obtain a new guess for the minimum.

5. Repeat steps 2--4 until the algorithm converges to a minimum.



Master gradient descent algorithm for minimizing loss with the right learning rate and initialization in this lesson.

Gradient Descent

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

Gradient Descent

What is gradient descent?