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Hands-On Machine Learning with OpenCV 4

Video Description

Expand your OpenCV knowledge and use machine learning to your advantage with this practical hands-on course!

About This Video

  • This course provides you with the latest and greatest developments that Machine Learning brings to Computer Vision.
  • A practical, hands-on course with a ton of real-life examples to help you build your own solutions.
  • Learn about cutting-edge and state-of-the-art (and ever popular) object detection.

In Detail

Computer Vision has been booming in the past few years and it has become a highly sought-after skill. There are tons of real-life problems for which Machine Learning-based solutions provide significantly better results than traditional ad-hoc approaches. The application of Machine Learning and Deep Learning is rapidly gaining significance in Computer Vision.

All the latest tech—from self-driving cars to autonomous drones—uses AI running on images and videos. If you want to get your hands dirty with this technology and use it to craft your own unique solutions, then look no further because this course is perfect for you!

This hands-on course will immerse you in Machine Learning, and you'll learn about key topics and concepts along the way. This course is perfect for people who wish to explore the possibilities inherent in Machine Learning.

The code bundle for this course is available at - https://github.com/PacktPublishing/Hands-On-Machine-Learning-with-OpenCV-4

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Chapter 1 : Course Overview and Introduction
    1. The Course Overview 00:03:27
    2. Introduction to Machine Learning in Computer Vision 00:04:11
    3. Setting Up the Development Environment 00:03:09
  2. Chapter 2 : Basics of Image Processing Using OpenCV
    1. Reading Images and Video Feeds 00:06:19
    2. Manipulating Image Properties — Color Spaces, Thresholding 00:06:26
    3. Exploring the Drawing Functions of OpenCV 00:03:19
  3. Chapter 3 : Machine Learning on Images: The Supervised Approach – KNNs and SVMs
    1. Understanding Supervised Learning 00:02:22
    2. A Quick Comparison – KNN versus SVM 00:04:57
    3. Visualizing the Quick, Draw! Dataset and Establishing the ML Pipeline 00:06:06
    4. Classifying Hand-Made Sketches Using KNN and SVM 00:08:38
  4. Chapter 4 : The Unsupervised Approach — Clustering with K-Means
    1. How Unsupervised Learning Is Different 00:02:03
    2. Clustering and the K- Means Algorithms 00:04:29
    3. Using K-Means to Cluster the Quick, Draw! Dataset 00:08:54
  5. Chapter 5 : Object Detection Using Histograms and Haar Cascades
    1. Understanding Histograms and Backprojection 00:03:32
    2. Detecting Objects in Real Time Using Colour 00:10:17
    3. Understanding What a Haar Cascade is 00:02:13
    4. Detecting Objects in Real Time Using Haar Cascades 00:04:43
  6. Chapter 6 : Convolutional Neural Networks for Object Detection
    1. CNNs - What the Hype Is About 00:04:05
    2. Using a Pre-Trained Caffe Model for Object Detection 00:10:10
  7. Chapter 7 : Training a CNN to Detect Custom Objects
    1. Using the TensorFlow Object Detection API 00:03:10
    2. Gathering the Dataset and Annotating the Images 00:06:26
    3. Generate TFRecords and Train 00:09:15
    4. Export the Inference Graph and Test the Model 00:04:57