Learn machine learning with scikit-learn, covering supervised learning, clustering, regression, SVMs, autoencoders, and ensemble methods through practical Python projects.

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Implementation of NN

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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

The above illustration shows the multiclass classification of the Iris dataset using SVM.
## One-vs-all
Multiclass SVM is an extension of binary SVM, where the SVM is trained to classify data into more than two classes. There are several approaches to training a multiclass SVM, but the most common approach is to use the **one-vs-all (OVA)** method. In the OVA method, a separate binary SVM is trained for each class, with the data of that class as the positive class and the data of all other classes as the negative class. The SVM with the highest output score is chosen as the predicted class during testing.



> **Note:** Both the hard-margin and soft-margin SVM algorithms perform poorly for multiclass classification. These are inherently binary classification algorithms. The multiclass extension is a hack and has no probabilistic interpretation, unlike Relevance Vector Machines, which are non-convex.
## Algorithm
Here are the steps to train a multiclass SVM using the OVA method:

1. Collect the data for each class and preprocess it as necessary.

2. Split the data into training and testing sets. The training set will be used to train the SVM, and the testing set will be used to evaluate its performance.

3. Train a binary SVM for each class using the training set. The training class data is used as the positive class, and the data of all other classes are used as the negative class.

4. During testing, each binary SVM is used to assign a score indicating the likelihood of the input belonging to the positive class. The SVM with the highest score is chosen as the predicted class.


In this lesson, we'll explore how to implement SVM in `sklearn`.


## Import the necessary libraries
To get started, we need to import the necessary libraries. We'll be using NumPy for numerical computations, Matplotlib for data visualization, and scikit-learn for SVM implementation.


## Load dataset
For this lesson, we'll use the famous Iris dataset, which is a multiclass classification dataset. We can load the dataset using the `load_iris()` function from `sklearn`.

Learn how to implement SVM in sklearn using the one-vs-all approach for multiclass classification.

SVM in Sklearn

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

SVM in Sklearn

Multiclass classification

One-vs-all