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This webinar was recorded 20200609 at am New York Time
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Awesome song and introduction
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Import Modules
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Import Data
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Missing Data Part 1: Identifying
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Missing Data Part 2: Dealing with it
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Downsampling the data
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Format Data Part 1: X and y
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Format Data Part 2: One-Hot Encoding
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Format Data Part 3: Centering and Scaling
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Build a Preliminary SVM
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Optimize Parameters with Cross Validation GridSearchCV
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Build and Draw Final SVM
Description:
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.

Support Vector Machines in Python from Start to Finish

StatQuest with Josh Starmer
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#Computer Science #Machine Learning #Support Vector Machine (SVM) #Programming #Programming Languages #Python #Data Science #Data Preprocessing #Cross-Validation
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This webinar was recorded 20200609 at am New York Time