by Michael Beyeler | Released May 2018 | ISBN: 9781789340525

A practical introduction to the world of machine learning and image processing using OpenCV and PythonAbout This VideoUnderstand, perform, and experiment with machine learning techniques using this easy-to-follow guideGrasp the advanced concepts of bootstrapping, boosting, voting, and baggingEvaluate, 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, computer vision has been widely used in various domains.This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems.All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Advanced-Methods-and-Deep-LearningThe course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems. Show and hide more

1. Chapter 1 : Implementing a Spam Filter with Bayesian Learning
   • The Course Overview 00:02:15
   • Understanding and Implementing Bayesian Classifier 00:09:27
   • Classifying Emails Using Naive Bayes Classifier 00:12:50
2. Chapter 2 : Discovering Hidden Structures with Unsupervised Learning
   • Understanding Unsupervised Learning and k-means Clustering 00:04:35
   • Understanding Expectation-Maximization 00:09:20
   • Compressing Color Spaces Using k-means 00:05:51
   • Classifying Handwritten Digits Using k-means 00:03:24
   • Organizing Clusters as a Hierarchical Tree 00:02:08
3. Chapter 3 : Using Deep Learning to Classify Handwritten Digits
   • Understanding and Implementing Perceptron 00:09:32
   • Understanding and Implementing Multilayer Perceptrons 00:07:37
   • Getting Acquainted with Deep Learning 00:04:22
   • Classifying Handwritten Digits 00:08:00
4. Chapter 4 : Combining Different Algorithms into an Ensemble
   • Understanding Ensemble Methods 00:10:07
   • Combining Decision Trees into a Random Forest 00:09:17
   • Using Random Forests for Face Recognition 00:04:14
   • Implementing AdaBoost 00:02:51
   • Combining Different Models into a Voting Classifier 00:03:30
5. Chapter 5 : Selecting the Right Model with Hyperparameter Tuning
   • Evaluating a Model 00:06:55
   • Understanding Cross-Validation 00:05:15
   • Estimating Robustness Using Bootstrapping 00:04:39
   • Assessing the Significance of Our Results 00:06:55
   • Tuning Hyperparameters with Grid Search 00:07:11
   • Chaining Algorithms Together to Form a Pipeline 00:05:18
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