Book Content
chapters • 14h total length
1. A Taste of Machine Learning
2. Working with Data in OpenCV
3. First Steps in Supervised Learning
4. Representing Data and Engineering Features
5. Using Decision Trees to Make a Medical Diagnosis
6. Detecting Pedestrians with Support Vector Machines
7. Implementing a Spam Filter with Bayesian Learning
8. Discovering Hidden Structures with Unsupervised Learning
9. Using Deep Learning to Classify Handwritten Digits
10. Ensemble Methods for Classification
11. Selecting the Right Model with Hyperparameter Tuning
12. Using OpenVINO with OpenCV
13. Conclusion
