Hands-On Machine Learning with OpenCV 4

Hands-On Machine Learning with OpenCV 4

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 2h 03m | 415 MB

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

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.

Its hands-on, practical approach ensures that this course is never boring and that the viewer learns through interactive examples and apps. All necessary information is conveyed as and when required, with an emphasis on where to apply it in real life. One key concept is covered in every video, along with a sample code snippet to demonstrate its usage. The course builds its way up and gradually moves from Machine Learning to Deep Learning techniques.

What You Will Learn

  • Become more experienced with using OpenCV and its vast in-built functionalities
  • Apply various Machine Learning algorithms to real-life problems
  • Explore Supervised Learning and Unsupervised Learning approaches in Computer Vision
  • Master the standard template/pipeline while designing ML solutions
  • Work with various datasets and visualize them
  • Get familiar with multiple ways to perform real-time object detection
  • Train your own custom image classifier using Convolutional Neural Networks
Table of Contents

01 The Course Overview
02 Introduction to Machine Learning in Computer Vision
03 Setting Up the Development Environment
04 Reading Images and Video Feeds
05 Manipulating Image Properties — Color Spaces, Thresholding
06 Exploring the Drawing Functions of OpenCV
07 Understanding Supervised Learning
08 A Quick Comparison – KNN versus SVM
09 Visualizing the Quick, Draw! Dataset and Establishing the ML Pipeline
10 Classifying Hand-Made Sketches Using KNN and SVM
11 How Unsupervised Learning Is Different
12 Clustering and the K- Means Algorithms
13 Using K-Means to Cluster the Quick, Draw! Dataset
14 Understanding Histograms and Backprojection
15 Detecting Objects in Real Time Using Colour
16 Understanding What a Haar Cascade is
17 Detecting Objects in Real Time Using Haar Cascades
18 CNNs – What the Hype Is About
19 Using a Pre-Trained Caffe Model for Object Detection
20 Using the TensorFlow Object Detection API
21 Gathering the Dataset and Annotating the Images
22 Generate TFRecords and Train
23 Export the Inference Graph and Test the Model