This document provides an overview of machine learning concepts and processes. It discusses types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. It also outlines the typical machine learning process of data collection/preparation, modeling, training models, evaluating results, and improving models. Specific examples discussed include using machine learning for email marketing conversion prediction, rhythmic gymnastics image classification, and recommendation systems.
15. Process
Data
Model 1 Model 2 Model N………
Results 1 Results 2 Results N
Reducing
features
size
Scaling
…
other data
stuffBest result
Train model
(parameters)
………
17. Next level
• Features
• Volume
• Understand Model parameters
• Train model harder (24/7)
• Whole picture: not only 1 score, but Precision,
Recall, f1-score, etc.
18. Lessons & Knowledge source
• Think about features (valuable VS lots VS less) balance
• Models are sensitive to different data
• Model tuning is important, but long road
• Sources:
• O’REILLY: Introduction to Machine Learning with Python
• scikit-learn.org
• Github
26. Approach
• Collect data
Simple iPhone app that helps draw and export
• Prepare data
Image = Grid. Each cell = 1 (black) or 0 (white)
Convert Grid to Line
Image = 000100011000011100011…
• Train + Analyze
Until satisfied with the score