Machine Learning for
OpenCV 4
Second Edition
OpenCV 4
Second Edition
Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn
BIRMINGHAM - MUMBAI
Title Page
by Michael Beyeler, Vishwesh Ravi Shrimali, Aditya Sharma
Machine Learning for OpenCV 4 - Second Edition
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Fundamentals of Machine Learning and OpenCV
- A Taste of Machine Learning
- Working with Data in OpenCV
- Technical requirements
- Understanding the machine learning workflow
- Dealing with data using OpenCV and Python
- Summary
- First Steps in Supervised Learning
- Representing Data and Engineering Features
- Section 2: Operations with OpenCV
- Using Decision Trees to Make a Medical Diagnosis
- Technical requirements
- Understanding decision trees
- Using decision trees to diagnose breast cancer
- Using decision trees for regression
- Summary
- Detecting Pedestrians with Support Vector Machines
- Implementing a Spam Filter with Bayesian Learning
- Discovering Hidden Structures with Unsupervised Learning
- Technical requirements
- Understanding unsupervised learning
- Understanding k-means clustering
- Understanding expectation-maximization
- Compressing color spaces using k-means
- Classifying handwritten digits using k-means
- Organizing clusters as a hierarchical tree
- Summary
- Section 3: Advanced Machine Learning with OpenCV
- Using Deep Learning to Classify Handwritten Digits
- Ensemble Methods for Classification
- Selecting the Right Model with Hyperparameter Tuning
- Technical requirements
- Evaluating a model
- Understanding cross-validation
- Estimating robustness using bootstrapping
- Assessing the significance of our results
- Tuning hyperparameters with grid search
- Scoring models using different evaluation metrics
- Chaining algorithms together to form a pipeline
- Summary
- Using OpenVINO with OpenCV
- Conclusion
- Other Books You May Enjoy
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