by Michael Beyeler | Released April 2018 | ISBN: 9781789347357

Learn the core concepts of the OpenCV and Machine LearningAbout This VideoUnderstand, perform, and experiment with Machine Learning techniques using this easy-to-follow guideGrasp the fundamental concepts of classification, regression, and clusteringEvaluate, compare, and choose the right algorithm for any taskLoad, store, edit, and visualize data using OpenCV and PythonIn DetailComputer 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.All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Supervised-Learning Show and hide more

1. Chapter 1 : A Taste of Machine Learning
   • The Course Overview 00:02:27
   • Getting Started with Machine Learning, Python, OpenCV 00:02:58
   • Installation 00:10:21
2. Chapter 2 : Working with Data in OpenCV and Python
   • Dealing with Data Using OpenCV and Python 00:02:57
   • Dealing with Data Using Python's NumPy Package 00:11:08
   • Visualizing the Data Using Matplotlib 00:08:04
3. Chapter 3 : First Steps in Supervised Learning
   • Understanding Supervised Learning 00:14:03
   • Using Classification Models to Predict Class labels 00:10:27
   • Using Regression Models to Predict Continuous Outcomes 00:07:36
   • Classifying Iris Species Using Logistic Regression 00:06:25
4. Chapter 4 : Representing Data and Engineering Features
   • Understanding Feature Engineering and Preprocessing Data 00:08:13
   • Implementing PCA, ICA, and NMF in OpenCV 00:06:54
   • Representing Categorical Variables and Text Features 00:05:30
   • Representing Images 00:09:06
5. Chapter 5 : Using Decision Trees to Make a Medical Diagnosis
   • Understanding and Building Decision Trees 00:10:26
   • Visualizing a Trained Decision Tree 00:07:05
   • Using Decision Trees to Diagnose Breast Cancer 00:05:08
   • Using Decision Trees for Regression 00:03:40
6. Chapter 6 : Detecting Pedestrians with Support Vector Machines
   • Understanding Linear Support Vector Machines 00:08:32
   • Dealing with Nonlinear Decision Boundaries 00:04:37
   • Detecting Pedestrians in the Wild 00:14:02
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