Learn to implement Support Vector Machines (SVMs) in Python from start to finish in this comprehensive 45-minute webinar. Explore essential steps including importing modules and data, handling missing data, downsampling, formatting data with one-hot encoding and scaling, building a preliminary SVM, optimizing parameters using cross-validation, and constructing the final SVM. Gain practical insights into machine learning techniques and enhance your data science skills with hands-on examples and explanations.
Overview
Syllabus
This webinar was recorded 20200609 at am New York Time
Awesome song and introduction
Import Modules
Import Data
Missing Data Part 1: Identifying
Missing Data Part 2: Dealing with it
Downsampling the data
Format Data Part 1: X and y
Format Data Part 2: One-Hot Encoding
Format Data Part 3: Centering and Scaling
Build a Preliminary SVM
Optimize Parameters with Cross Validation GridSearchCV
Build and Draw Final SVM
Taught by
StatQuest with Josh Starmer
