Machine Learning and AI: Support Vector Machines in Python

Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression

Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Created by: Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

What Will I Learn?

• Apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysis
• Understand the theory behind SVMs from scratch (basic geometry)
• Use Lagrangian Duality to derive the Kernel SVM
• Understand how Quadratic Programming is applied to SVM
• Support Vector Regression
• Polynomial Kernel, Gaussian Kernel, and Sigmoid Kernel
• Build your own RBF Network and other Neural Networks based on SVM

Requirements

• Calculus, Matrix Arithmetic / Geometry, Basic Probability
• Python and Numpy coding
• Logistic Regression

Target audience

• Beginners who want to know how to use the SVM for practical problems
• Experts who want to know all the theory behind the SVM
• Professionals who want to know how to effectively tune the SVM for their application