21.11.2024
Amazing Course !
Machine Learning with Support Vector Machines
with
Elitsa Kaloyanova
Master Support Vector Machines (SVMs): from theoretical foundations to practical applications
1 hour of content
3764 students
Start for free
What you get:
- 1 hour of content
- 14 Interactive exercises
- 10 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
Start for free
What you get:
- 1 hour of content
- 14 Interactive exercises
- 10 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Start for free
What you get:
- 1 hour of content
- 14 Interactive exercises
- 10 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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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
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.
Curriculum
- 1. Introduction to Support Vector Machines5 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 minIntroduction to Support Vector Machines5 minLinearly separable classes - hard margin problem5 minNon-linearly separable classes - soft margin problem5 minKernels - Intuition6 min - 2. Setting up the Environment2 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 now1 minInstalling the relevant packages1 min - 3. Support Vector Classifier - Practical Example11 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 case4 minPreprocessing the data3 minSplitting the data into train and test and rescaling2 minImplementing a linear SVM2 minImplementing a linear SVM: Assignment Read now1 minAnalyzing the results– Confusion Matrix, Precision, and Recall5 minAnalyzing the results– Confusion Matrix, Precision, and Recall: Assignment Read now1 minCross-validation6 minChoosing the kernels and C values for cross-validation3 minHyperparameter tuning using GridSearchCV4 minVisualizing Decision Boundaries: Assignment Read now1 min
Topics
PythonTheoryProgrammingSoft marginKernelsClassificationSupport Vector Machinesmachine learningHard MarginGridSearch
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.
Meet Your Instructor
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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