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- (Reference Guide) Machine Learning for OpenCV
Book Description
Machine Learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of Machine Learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind.
OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and Machine Learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for.
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.
By the end of this book, you will be ready to take on your own Machine Learning problems, either by building on the existing source code or developing your own algorithm from scratch!
What You Will Learn
- Explore and make effective use of OpenCV's Machine Learning module
- Learn deep learning for computer vision with Python
- Master linear regression and regularization techniques
- Classify objects such as flower species, handwritten digits, and pedestrians
- Explore the effective use of support vector machines, boosted decision trees, and random forests
- Get acquainted with neural networks and Deep Learning to address real-world problems
- Discover hidden structures in your data using k-means clustering
- Get to grips with data pre-processing and feature engineering
Table of Contents
1: A Taste of Machine Learning
- Getting started with machine learning
- Problems that machine learning can solve
- Getting started with Python
- Getting started with OpenCV
- Installation
- Summary
2: Working with Data in OpenCV and Python
- Understanding the machine learning workflow
- Dealing with data using OpenCV and Python
- Summary
3: First Steps in Supervised Learning
- Understanding supervised learning
- Using classification models to predict class labels
- Using regression models to predict continuous outcomes
- Using regression models to predict continuous outcomes
- Classifying iris species using logistic regression
- Summary
4: Representing Data and Engineering Features
- Understanding feature engineering
- Preprocessing data
- Understanding dimensionality reduction
- Representing categorical variables
- Representing text features
- Representing images
- Summary
5: Using Decision Trees to Make a Medical Diagnosis
- Understanding decision trees
- Using decision trees to diagnose breast cancer
- Using decision trees for regression
- Summary
6: Detecting Pedestrians with Support Vector Machines
- Understanding linear support vector machines
- Dealing with nonlinear decision boundaries
- Detecting pedestrians in the wild
- Summary
7: Implementing a Spam Filter with Bayesian Learning
- Understanding Bayesian inference
- Implementing your first Bayesian classifier
- Classifying emails using the naive Bayes classifier
- Summary
8: Discovering Hidden Structures with Unsupervised Learning
- 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
9: Using Deep Learning to Classify Handwritten Digits
- Understanding the McCulloch-Pitts neuron
- Understanding the perceptron
- Implementing your first perceptron
- Understanding multilayer perceptrons
- Getting acquainted with deep learning
- Classifying handwritten digits
- Summary
10: Combining Different Algorithms into an Ensemble
- Understanding ensemble methods
- Combining decision trees into a random forest
- Using random forests for face recognition
- Implementing AdaBoost
- Combining different models into a voting classifier
- Summary
11: Selecting the Right Model with Hyperparameter Tuning
- 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
12: Wrapping Up
- Approaching a machine learning problem
- Building your own estimator
- Where to go from here?
- Summary
SKU | 031018S |
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Weight | 1.9830 |
Coming Soon | No |
Days of Training | No |
Audience | Student |
Product Family | Partnerware |
Product Type | Print Courseware |
Electronic | No |
ISBN | 1783980284 |
Language | English |
Page Count | 368 |
Curriculum Library | No |
Year | No |
Manufacturer's Product Code | No |
Current Revision | 1.0 |
---|---|
Revision Notes | No Revision Information Available |
Original Publication Date | 2018-10-11 00:00:00 |