Hi everyone!
My name is Elitsa Kaloyanova. I’m a Computational Biologist turned data scientist with deep expertise in the fields of algorithms and data structures, phylogenetics, as well as population genetics.
And today is a very special day for me, as it marks the launch of my 4th course in the 365 Data Science Program – Machine Learning with Support Vector Machines!
In this post, I’ll share with you why Support Vector Machines is a topic every aspiring data science professional should explore. I’ll introduce you to the features of the course, what you can expect to learn from it, and how you can access the first 30 minutes of the course for free.
The 365 Data Science Machine Learning with Support Vector Machines Course
Why Machine Learning with Support Vector Machines?
Support Vector Machines (SVMs) are one of the most powerful techniques in supervised learning. They can be applied to both classification and regression tasks and have a wide range of applications, including bioinformatics, face detection, handwriting recognition, and many more. What’s more, SVMs kernel functions fit different data distributions at a reduced computational cost, making them highly flexible and efficient. In this course, you’ll grasp the theory behind Support Vector Machines and how to implement and optimize a Support Vector Classifier in Python using sk-learn.
Who Is This Course for?
This course is perfect for anyone who wants to level up their machine learning skills, earn a verifiable certificate of achievement, and expand their career opportunities in the data science field.
What Will You Learn in This Course?
Under my guidance, you will learn:
- The theory behind Support Vector Machines
- Soft margin problem
- Hard margin problem
- The intuition behind kernels
- Support Vector Classifier in sk-learn
- Cross-validation with GridSearchCV in Python
I've also included detailed course notes and exercises, as well as a practice exam and a course exam, to help you further improve your understanding of the topic.
Ready to Start Machine Learning with Support Vector Machines?
Visit the Machine Learning with Support Vector Machines course page to find more details about its curriculum or sign up below to watch 30 minutes for free.
