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- 43% Machine Learning and AI: Support Vector Machines in Python

Machine Learning and AI: Support Vector Machines in Python

$16.99Track price

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8.5/10 (Our Score)
Product is rated as #125 in category Machine Learning

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

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Specification: Machine Learning and AI: Support Vector Machines in Python

Duration

9 hours

Year

2021

Level

Expert

Certificate

Yes

Quizzes

No

1 review for Machine Learning and AI: Support Vector Machines in Python

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  1. 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.

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    Machine Learning and AI: Support Vector Machines in Python
    Machine Learning and AI: Support Vector Machines in Python

    $16.99

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