Support Vector Machines in Python, From Start to Finish

In this comprehensive course, you will master the implementation of Support Vector Machines in Python for predictive analysis of heart disease. Utilizing scikit-learn, you will delve into data manipulation, parameter optimization, and model evaluation. With hands-on projects on Rhyme's cloud platform, you will gain practical experience in building, evaluating, and interpreting support vector machines. This course is designed for Python programmers seeking proficiency in SVM, RBF, regularization, cross-validation, and confusion matrices.

• Import and manipulate data using pandas dataframe
• Format data for support vector machine, including One-Hot Encoding and handling missing data
• Optimize parameters for RBF and classification
• Build, evaluate, draw, and interpret a support vector machine