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

estimate the likelihood of a data point belonging to a class


reduce the dimensionality of the feature space

**(Fill in the blank.)** Probabilistic models are used in logistic regression to ________.

(Fill in the blank.) Probabilistic models are used in logistic regression to ________.

It lacks the ability to handle multiple output classes.


It requires the input features to be normalized.


The decision boundary it produces is always linear.


It assumes that the data points are clustered in distinct regions.


Why is logistic regression not suitable for handling nonlinearly separable data in its original form?

Why is logistic regression not suitable for handling nonlinearly separable data in its original form?

It allows the model to learn nonlinear decision boundaries in the input space.


It speeds up the convergence of the optimization algorithm.


It reduces the complexity of the model by reducing the number of features.

In the context of kernel logistic regression, what is the significance of the kernel trick?

In the context of kernel logistic regression, what is the significance of the kernel trick?

To maximize the number of misclassified instances


To find the weights that minimize the loss function


To find the global maximum of the loss function

In the optimization of binary cross-entropy (BCE) loss, what is the goal of the algorithm?

In the optimization of binary cross-entropy (BCE) loss, what is the goal of the algorithm?

**(True or False)** The goal of logistic regression is to minimize the mean squared error (MSE) between predicted and actual values.

(True or False) The goal of logistic regression is to minimize the mean squared error (MSE) between predicted and actual values.

**(True or False)** Logistic regression can be considered a simple neural network with no hidden layers.

(True or False) Logistic regression can be considered a simple neural network with no hidden layers.

**(True or False)** Multiclass logistic regression uses multiple independent models to classify each class against the others.

(True or False) Multiclass logistic regression uses multiple independent models to classify each class against the others.

Let’s put your knowledge to the test with this quick quiz on the key concepts of Logistic regression.

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

Quiz on Logistic Regression