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Machine Learning with Support Vector Machines

with Elitsa Kaloyanova
4.8/5
(588)

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

1 hour of content 3885 students

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 1 hour of content
  • 14 Interactive exercises
  • 16 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Machine Learning with Support Vector Machines

A course by Elitsa Kaloyanova

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 1 hour of content
  • 14 Interactive exercises
  • 16 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

$99.00

Lifetime access

Buy now

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 1 hour of content
  • 14 Interactive exercises
  • 16 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • Master Support Vector Machines to elevate your data analysis skills to the next level
  • Fully grasp the inner workings of Support Vector Machines as well as their practical application
  • Understand the pros and cons of the SVM algorithm to make informed decisions in model selection
  • Build and optimize classification models using Support Vector Machines and learn why they are indispensable when it comes to understanding the problem at hand
  • Integrate essential math concepts with hands-on Python programming skills
  • Develop the skills to independently plan, execute, and deliver a complete ML project from start to finish

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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.
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What does the course cover?

1.1 What does the course cover?

4 min

Introduction to Support Vector Machines

1.2 Introduction to Support Vector Machines

5 min

Linearly separable classes - hard margin problem

1.4 Linearly separable classes - hard margin problem

5 min

Non-linearly separable classes - soft margin problem

1.6 Non-linearly separable classes - soft margin problem

5 min

Kernels - Intuition

1.8 Kernels - Intuition

6 min

Setting up the environment

2.1 Setting up the environment

1 min

Interactive Exercises

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

true-false exercise
single-choice exercise
multiple-choice exercise
flashcards exercise
fill-blank-typing exercise
fill-blank-selection exercise
order exercise
categorize exercise

Curriculum

  • 1. Introduction to Support Vector Machines
    5 Lessons 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?
    4 min
    Introduction to Support Vector Machines
    5 min
    Linearly separable classes - hard margin problem
    5 min
    Non-linearly separable classes - soft margin problem
    5 min
    Kernels - Intuition
    6 min
  • 2. Setting up the Environment
    2 Lessons 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 Read now
    1 min
    Installing the relevant packages
    1 min
  • 3. Support Vector Classifier - Practical Example
    11 Lessons 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
    4 min
    Preprocessing the data
    3 min
    Splitting the data into train and test and rescaling
    2 min
    Implementing a linear SVM
    2 min
    Implementing a linear SVM: Assignment Read now
    1 min
    Analyzing the results– Confusion Matrix, Precision, and Recall
    5 min
    Analyzing the results– Confusion Matrix, Precision, and Recall: Assignment Read now
    1 min
    Cross-validation
    6 min
    Choosing the kernels and C values for cross-validation
    3 min
    Hyperparameter tuning using GridSearchCV
    4 min
    Visualizing Decision Boundaries: Assignment Read now
    1 min

Topics

PythonTheoryProgrammingSoft marginKernelsClassificationSupport Vector MachinesMachine LearningHard MarginGridSearchMachine and Deep Learning

Tools & Technologies

python

Course Requirements

  • You need to complete an introduction to Python before taking this course
  • Basic skills in statistics, probability, and linear algebra are required
  • It is highly recommended to take the Machine Learning in Python course first
  • You will need to install the Anaconda package, which includes Jupyter Notebook

Who Should Take This Course?

Level of difficulty: Intermediate

  • Aspiring data scientists and ML engineers

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Elitsa Kaloyanova

Elitsa Kaloyanova

Senior Data Scientist at

7 Courses

3152 Reviews

50744 Students

Elitsa Kaloyanova is a Computational Biologist, with significant expertise in the fields of algorithms, data structures, phylogenetics, and population genetics. She has a solid academic background in Bioinformatics with publications on constructing Phylogenetic Networks and Trees. In 2021, she led 365’s effort to create practice exams and course exams for each course included in the program. Elitsa was able to successfully coordinate with several types of stakeholders and performed superior Quality Assurance.

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What Our Learners Say

Dariush Azizi
Dariush Azizi
11.02.2025
Ritik Jain
Ritik Jain
04.02.2025

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