Online Course
Machine Learning with Support Vector Machines

Name: Machine Learning Course: Support Vector Machines
Price: 36 USD

with Elitsa Kaloyanova

Master Support Vector Machines (SVMs): from theoretical foundations to practical applications

4.8

862 reviews on

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4,352 students already enrolled

Skill level:

Intermediate

Duration:

1 hour

Lessons (1 hour)

Practice exams (20 minutes)

CPE credits:

2

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Master Support Vector Machines for advanced data analysis.
Fully grasp their inner workings and practical applications.
Understand the pros and cons of the SVM algorithm.
Build and optimize classification models with SVM.
Integrate math concepts with hands-on Python programming.

Topics & tools

PythonTheoryProgrammingSoft marginKernelsClassificationSupport Vector MachinesMachine LearningHard MarginGridSearchMachine and Deep Learning

Your instructor

Elitsa Kaloyanova

Senior Data Scientist at 365 Data Science | Computational Biologist | Data Visualization Expert

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Bioinformatics

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Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 2 Field of Study: Information Technology
Delivery Method: QAS Self Study

This course is all about Support Vector Machines – one of the most versatile and widely used techniques in supervised learning. They can be applied to both classification and regression tasks and thanks to kernels, fit different data distributions at a reduced computational cost . In this course, you’ll grasp the theory behind support vector machines andhow to implement and optimize a Support Vector Classifier in Python using sk-learn.

Prerequisites

Python (version 3.8 or later), Streamlit library, OpenAI API key, and a code editor or IDE (e.g., VS Code or Jupyter Notebook)
Intermediate Python skills are required.
Familiarity with basic statistics and linear algebra is helpful but not mandatory.

Advanced preparation

Curriculum

18 lessons 14 exercises 2 exams

1. Introduction to Support Vector Machines

25 min

We introduce the concept of support vector machines and detail the hard and soft margin case for classification using support vectors. We also touch upon the main kernels which are used in support vector machines, which are an essential part of the versatility and power of the support vector classifier.

25 min

We introduce the concept of support vector machines and detail the hard and soft margin case for classification using support vectors. We also touch upon the main kernels which are used in support vector machines, which are an essential part of the versatility and power of the support vector classifier.

What does the course cover? Free

Introduction to Support Vector Machines Free

Exercise Free

Linearly separable classes - hard margin problem Free

Exercise Free

Non-linearly separable classes - soft margin problem Free

Exercise Free

Kernels - Intuition Free

Exercise Free
2. Setting up the Environment

2 min

Section two covers the installation process for all the Python packages you will need to progress with a practical example. If you’re just starting out with the language, we recommend checking out our Introduction to Jupyter course which provides details on how to install Anaconda and navigating the Jupyter Environment.

2 min

Section two covers the installation process for all the Python packages you will need to progress with a practical example. If you’re just starting out with the language, we recommend checking out our Introduction to Jupyter course which provides details on how to install Anaconda and navigating the Jupyter Environment.

Setting up the environment Free

Installing the relevant packages Free
3. Support Vector Classifier - Practical Example

32 min

In this section, you will apply in practice all the theoretical knowledge gained in the previous sections and learn how to implement a support vector classifier using sk-learn in Python. The classification data consists of the characteristics of mushrooms which we identify as either edible or poisonous. We also rely on grid search cross validation to improve the performance of our model.

32 min

In this section, you will apply in practice all the theoretical knowledge gained in the previous sections and learn how to implement a support vector classifier using sk-learn in Python. The classification data consists of the characteristics of mushrooms which we identify as either edible or poisonous. We also rely on grid search cross validation to improve the performance of our model.

Intro to the practical case

Preprocessing the data

Splitting the data into train and test and rescaling

Implementing a linear SVM

Implementing a linear SVM: Assignment

Exercise

Analyzing the results– Confusion Matrix, Precision, and Recall

Analyzing the results– Confusion Matrix, Precision, and Recall: Assignment

Exercise

Cross-validation

Choosing the kernels and C values for cross-validation

Hyperparameter tuning using GridSearchCV

Visualizing Decision Boundaries: Assignment

Practice exam
4. Course exam

25 min

25 min

Course exam

Free lessons

1.1 What does the course cover?

4 min

1.2 Introduction to Support Vector Machines

5 min

1.4 Linearly separable classes - hard margin problem

5 min

1.6 Non-linearly separable classes - soft margin problem

5 min

1.8 Kernels - Intuition

6 min

2.1 Setting up the environment

1 min

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