Mastering SVM: A Comprehensive Guide with Code in Python

V-Support vector machine, slack variables, Support Vector Regression (SVR), Kernel Trick

Created by: Hoang Quy La
Electrical Engineer

What Will I Learn?

• Maximum margin
• slack variables
• Data preprocessing
• Standardizing features
• Overfitting
• Train the model
• Kernel Trick
• C parameter in support vector machine
• Linear Classification in SVM
• Non-linear SVM implementation
• V-Support vector machine
• Support Vector Regression (SVR)
• Confusion matrix
• Splitting the datasets into training and testing sets

Requirements

• Python knowledge and basic machine learning is required

Target audience

• Anyone interested in Machine Learning.
• Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
• Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
• Any students in college who want to start a career in Data Science
• Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.