Packt Machine Learning for OpenCV Supervised Learning XQZT
Packt Machine Learning for OpenCV Supervised Learning-XQZT
English | Size: 1.01 GB
Computer vision is one of today’s most exciting application fields of Machine Learning, From self-driving cars to Medical diagnosis, this has been widely used in various domains.
This course will take you right from the essential concepts of statistical learning to help you with various algorithms to implement it with other OpenCV tasks.
The course will also guide you through creating custom graphs and visualizations, and show you how to go from the raw data to beautiful visualizations. We will also build a machine learning system that can make a medical diagnosis.
By the end of this course, you will be ready create your own ML system and will also be able to take on your own machine learning problems.
Style and Approach
This course walks you through the key elements of OpenCV and its powerful Machine Learning classes while demonstrating how to get to grips with a range of models.
Table of Contents
A TASTE OF MACHINE LEARNING
WORKING WITH DATA IN OPENCV AND PYTHON
FIRST STEPS IN SUPERVISED LEARNING
REPRESENTING DATA AND ENGINEERING FEATURES
USING DECISION TREES TO MAKE A MEDICAL DIAGNOSIS
DETECTING PEDESTRIANS WITH SUPPORT VECTOR MACHINES
What You Will Learn
Explore and make effective use of OpenCV’s Machine Learning module
Master linear regression and regularization techniques
Classify objects such as flower species and pedestrians
Creatively build decision trees in OpenCV
Explore the effective use of support vector machines, boosted decision trees, and random forests
Learn to visualize data with OpenCV and Python.