Machine Learning With Python OpenCV Free Video Course 2020

Machine learning with python , OpenCV , and Deep Learning!

Machine learning with python

What you will learn:

  • Understand basics of NumPy
  • Manipulate and open Images with NumPy
  • Use OpenCV to work with image files
  • Use Python and OpenCV to draw shapes on images and videos
  • Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations.
  • Create Color Histograms with OpenCV
  • Open and Stream video with Python and OpenCV
  • Detect Objects, including corner, edge, and grid detection techniques with OpenCV and Python
  • Create Face Detection Software
  • Segment Images with the Watershed Algorithm
  • Track Objects in Video
  • Use Python and Deep Learning to build image classifiers
  • Work with Tensorflow, Keras, and Python to train on your own custom images.

Description:

Welcome to a definitive online seminar on Python for Computer Vision!This course is your best asset for figuring out how to utilize the Python programming language for Computer Vision.We will investigate how to utilize Python and the OpenCV (Open Computer Vision) library to examine pictures and video information.

The most mainstream stages on the planet are creating at no other time seen measures of picture and video information. Like clockwork clients transfer over 300 hours of video to Youtube, Netflix supporters stream more than 80,000 hours of video, and Instagram clients like more than 2 million photographs! Presently like never before its essential for engineers to pick up the vital aptitudes to work with picture and video information utilizing PC vision.

PC vision permits us to dissect and use picture and video information, with applications in an assortment of enterprises, including self-driving vehicles, interpersonal organization applications, clinical diagnostics, and some more.As the quickest developing language in ubiquity, Python is appropriate to use the intensity of existing PC vision libraries to gain from this picture and video information.

In this course we will encourage you all that you have to know to turn into a specialist in PC vision! This $20 billion dollar industry will be one of the most significant activity markets in the years to come.

We will begin the course by finding out about numerical handling with the NumPy library and how to open and control pictures with NumPy. At that point will proceed onward to utilizing the OpenCV library to open and work with picture nuts and bolts. At that point we’ll begin to see how to process pictures and apply an assortment of impacts, including shading mappings, mixing, edges, angles, and that’s only the tip of the iceberg.

This course covers all this and more, including the following topics:

  • NumPy
  • Images with NumPy
  • Image and Video Basics with NumPy
  • Color Mappings
  • Blending and Pasting Images
  • Image Thresholding
  • Blurring and Smoothing
  • Morphological Operators
  • Gradients
  • Histograms
  • Streaming video with OpenCV
  • Object Detection
  • Template Matching
  • Corner, Edge, and Grid Detection
  • Contour Detection
  • Feature Matching
  • WaterShed Algorithm
  • Face Detection
  • Object Tracking
  • Optical Flow
  • Deep Learning with Keras
  • Keras and Convolutional Networks
  • Customized Deep Learning Networks
  • State of the Art YOLO Networks
  • and much more!
  • Feel free to message me on Udemy if you have any questions about the course!

Requirements for this course:

  • Must have clear understanding of Python Basics
  • Windows 10 or MacOS or Ubunt
  • Must have Install Permissions on Computer
  • WebCam if you want to learn the video streaming content

Course content Machine learning with python:

1. Course Overview and Introduction

  • Course Overview
  • FAQ – Frequently Asked Questions
  • Course Curriculum Overview
  • Getting Set-Up for the Course Content

2. NumPy and Image Basics

  • Introduction to Numpy and Image Section
  • NumPy Arrays
  • What is an image?
  • Images and NumPy
  • NumPy and Image Assessment Test
  • NumPy and Image Assessment Test – Solutions
  • Image Basics with OpenCV
  • Introduction to Images and OpenCV Basics
  • Opening Image files in a notebook
  • Opening Image files with OpenCV
  • Drawing on Images – Part One – Basic Shapes
  • Drawing on Images Part Two – Text and Polygons
  • Direct Drawing on Images with a mouse – Part One
  • Direct Drawing on Images with a mouse – Part Two
  • Direct Drawing on Images with a mouse – Part Three
  • Image Basics Assessment
  • Image Basics Assessment Solutions

3. Image Processing

  • Introduction to Image Processing
  • Color Mappings
  • Blending and Pasting Images
  • Blending and Pasting Images Part Two – Masks
  • Image Thresholding
  • Blurring and Smoothing
  • Blurring and Smoothing – Part Two
  • Morphological Operators
  • Gradients
  • Histograms – Part One
  • Histograms – Part Two – Histogram Eqaulization
  • Histograms Part Three – Histogram Equalization
  • Image Processing Assessment
  • Image Processing Assessment Solutions

4. Video Basics with Python and OpenCV

  • Introduction to Video Basics
  • Connecting to Camera
  • Using Video Files
  • Drawing on Live Camera
  • Video Basics Assessment
  • Video Basics Assessment Solutions

5. Object Detection with OpenCV and Python

  • Introduction to Object Detection
  • Template Matching
  • Corner Detection – Part One – Harris Corner Detection
  • Corner Detection – Part Two – Shi-Tomasi Detection
  • Edge Detection
  • Grid Detection
  • Contour Detection
  • Feature Matching – Part One
  • Feature Matching – Part Two
  • Watershed Algorithm – Part One
  • Watershed Algorithm – Part Two
  • Custom Seeds with Watershed Algorithm
  • Introduction to Face Detection
  • Face Detection with OpenCV
  • Detection Assessment
  • Detection Assessment Solutions

6. Object Tracking

  • Introduction to Object Tracking
  • Optical Flow
  • Optical Flow Coding with OpenCV – Part One
  • Optical Flow Coding with OpenCV – Part Two
  • MeanShift and CamShift Tracking Theory
  • MeanShift and CamShift Tracking with OpenCV
  • Overview of various Tracking API Methods
  • Tracking APIs with OpenCV

7. Deep Learning for Computer Vision

  • Introduction to Deep Learning for Computer Vision
  • Machine Learning Basics
  • Understanding Classification Metrics
  • Introduction to Deep Learning Topics
  • Understanding a Neuron
  • Understanding a Neural Network
  • Cost Functions
  • Gradient Descent and Back Propagation
  • Keras Basics
  • MNIST Data Overview
  • Convolutional Neural Networks Overview – Part One
  • Convolutional Neural Networks Overview – Part Two
  • Keras Convolutional Neural Networks with MNIST
  • Keras Convolutional Neural Networks with CIFAR-10
  • LINK FOR CATS AND DOGS ZIP
  • Deep Learning on Custom Images – Part One
  • Deep Learning on Custom Images – Part Two
  • Deep Learning and Convolutional Neural Networks Assessment
  • Deep Learning and Convolutional Neural Networks Assessment Solutions
  • Introduction to YOLO v3
  • YOLO Weights Download
  • YOLO v3 with Python

8. Capstone Project

  • Introduction to CapStone Project
  • Capstone Part One – Variables and Background function
  • Capstone Part Two – Segmentation
  • Capstone Part Three – Counting and ConvexHull
  • Capstone Part Four – Bringing it all together

Who this course is for:

  • Python Developers interested in Computer Vision and Deep Learning. This course is not for complete python beginners.

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