95% Off Machine Learning and AI: Support Vector Machines in Python | Online Course

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In this course, you will journey with Lazy Programmer is a data scientist, big data engineer, and full-stack software engineer will teach you Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression. Enrolling in course, Machine Learning and AI: Support Vector Machines in Python which is taught by Lazy Programmer.

Course: Machine Learning and AI: Support Vector Machines in Python
Author: Lazy Programmer Inc. is a data scientist, big data engineer, and full-stack software engineer. He’s currently the creator of Bestselling in Artificial Intelligence, Natural Language Processing, A/B Testing, TensorFlow, Python courses on Udemy, with over 212,000 students across his 23 Courses on Udemy.
Description: Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression.
Skill Level: Expert Level
Content: 9 hours on-demand video
Lessons: 72 lectures
Languages: English
Categories: Business, Data & Analytics, Machine Learning
Includes: Unlimited access – watch the course as many times as you wish!
There is no risk: 30-day money back guarantee! – try it risk-free! You have nothing to lose.
Learn Anywhere Available: Learn at your own pace wherever and whenever on the computer, mobile device, or tablet!
Recognition Certificate: Certificate of completion to present to your current or future employer!

Take the course, Machine Learning and AI: Support Vector Machines in Python. Here are some of the things you’ll be able to do after taking this course:
Upon completion of the course, you will be able to apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysis
Upon completing this course, you will understand the theory behind SVMs from scratch (basic geometry)
After taking this course, you will be able to use Lagrangian Duality to derive the Kernel SVM
Upon completing this course, you will understand how Quadratic Programming is applied to SVM
The student completing this course will be able to support Vector Regression
By the end of this course, you’ll know Polynomial Kernel, Gaussian Kernel, and Sigmoid Kernel
Once finished, you will know what how to build your own RBF Network and other Neural Networks based on SVM
And much more!

Who this course is for:
This course is perfect for beginners who want to know how to use the SVM for practical problems
This course is perfect for experts who want to know all the theory behind the SVM
This course is perfect for professionals who want to know how to effectively tune the SVM for their application, This course is an excellent choice!

Enroll in This Online Course Now!


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