Support Vector Machines (SVM) are one of the most powerful machine learning models around, and this topic has been one that students have requested ever since I started making courses.
These days, everyone seems to be talking about deep learning, but in fact there was a time when support vector machines were seen as superior to neural networks. One of the things you’ll learn about in this course is that a support vector machine actually is a neural network, and they essentially look identical if you were to draw a diagram.
The toughest obstacle to overcome when you’re learning about support vector machines is that they are very theoretical. This theory very easily scares a lot of people away, and it might feel like learning about support vector machines is beyond your ability. Not so!
In this course, we take a very methodical, step–by–step approach to build up all the theory you need to understand how the SVM really works. We are going to use Logistic Regression as our starting point, which is one of the very first things you learn about as a student of machine learning. So if you want to understand this course, just have a good intuition about Logistic Regression, and by extension have a good understanding of the geometry of lines, planes, and hyperplanes.
Instructor Details
Courses : 22
Specification: Machine Learning and AI: Support Vector Machines in Python
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1 review for Machine Learning and AI: Support Vector Machines in Python
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Price | $16.99 |
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Provider | |
Duration | 9 hours |
Year | 2022 |
Level | Expert |
Language | English |
Certificate | Yes |
Quizzes | No |
$29.99 $16.99
Nirav Mistry –
This course is definitely one of the best courses for the topic. I admire the practical approach of the course while not lacking in the technical details. There are some topics that I need to review my prior knowledge to further understand the reasoning, however overall I find myself often coming back to review the fine points. I can see the instructor is an expert, showing both the practical and theoretical sides of the topic.