Book Content
chapters • 12h44m total length
1. A Taste of Machine Learning
2. Working with Data in OpenCV and Python
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. Combining Different Algorithms Into an Ensemble
11. Selecting the Right Model with Hyperparameter Tuning
12. Wrapping Up
