Machine Learning for OpenCV
Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. Combined with your having learned to select the right tool for the task, this book will make sure you get comfortable with all relevant machine learning fundamentals.
Offered by
Difficulty Level
Intermediate
Completion Time
12h44m
Language
English
About Book
Who Is This Book For?
This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks.
Machine Learning for OpenCV
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h44m total length
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
Implementing a Spam Filter with Bayesian Learning
Discovering Hidden Structures with Unsupervised Learning
Using Deep Learning to Classify Handwritten Digits
Combining Different Algorithms Into an Ensemble
Selecting the Right Model with Hyperparameter Tuning
Wrapping Up
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