Complete Machine Learning Pipeline for 3D Vision | From Web Scraping to AWS Deployment

Описание: In this 18-minute tutorial, I walk you through a complete end-to-end machine learning pipeline for 3D object classification. We'll build a bottles classifier from scratch, covering every step from data collection to cloud deployment.

What you'll learn:

Web scraping techniques for collecting training data (HTML/CSS parsing)
3D feature extraction using PyVista (feature edges, Dijkstra algorithm)
Model selection with 20+ scikit-learn classifiers
Train/test split strategies and hold-out validation
K-Fold cross-validation for robust model evaluation
AWS deployment for production-ready ML models

Timestamps:
0:00 - Introduction
1:30 - Data Collection with Web Scraping
4:00 - 3D Feature Extraction (PyVista)
7:30 - Model Selection (sklearn classifiers)
10:00 - Train/Test Split & Hold-Out Method
12:30 - K-Fold Cross-Validation
15:00 - AWS Deployment
17:00 - Results & Conclusion

Tools & Libraries:
Python | PyVista | scikit-learn | BeautifulSoup | AWS | Open3D | NumPy | Pandas | Matplotlib

Resources:
GitHub Repository: https://github.com/1904jonathan/Parde...

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About this channel:
Applied Machine Learning for 3D Vision - practical tutorials on point cloud processing, 3D object classification, computer vision, and deploying ML models to production.

#MachineLearning #3DVision #Python #PyVista #ScikitLearn #AWS #ComputerVision #PointCloud #MLPipeline #DataScience #DeepLearning #ObjectClassification #WebScraping #CrossValidation #ModelSelection #Tutorial #MLOps