Computer Vision Intro™ OpenCV4 in Python with Deep Learning (Updated)

Learn by making 16 Computer VIsion Projects - Handwriting Recognition, Face Filters, Car Detectors & Classifiers & ALPR.

What you'll learn

How to build complex computer vision applications using the latest techniques in OpenCV
How to use Deep Learning using Keras & TensorFlow in Python
Face Detection & Recognition (face swapping and filters!)
Object Detection, Tracking and Motion Analysis
Learn to use Augmented Reality in Computer Vision
Programming skills such as basic python and numpy
Understand how to use computer vision in executing cool startup ideas
Detailed explanation of Neural and Convolutional Neural Networks
Build Simple Image Classifiers in Python

Requirements

Little to no programming knowledge is needed! I'll be showing you everything you need to know, but basic Python is a plus
You will need a computer running Microsoft Windows, or Linux, or a Mac running OS X.
All the software needed in this course is free and open source! I provide all images, models and classifiers used in this course
A webcam to implement some of the mini projects

Description

Master Computer Vision using newest version of OpenCV 4.0.1 in Python!

You'll learn the latest 2019 core computer vision concepts and implement 16 awesome projects!

New as of 2019:

A free Virtual Machine with OpenCV4.0.1 Contrib Package + Dlib and other essential libraries

New Project where you make your own Automatic Number-Plate Recognition (ALPR)

Core introduction to Deep Learning using Keras in Python (3+ hours of Deep Learning Computer Vision)

Deep Learning Computer Vision Content Includes:

A free Virtual Machine with all Deep Learning Python Libraries such as Keras and TensorFlow pre-installed

Detailed Explanations on Neural Networks and Convolutional Neural Networks

Understand how Keras works and how to use and create image datasets

Build a Handwritten Digit Classifier

Build a Multi Image Classifier

Build a Cats vs Dogs Classifier

Understand how to boost CNN performance using Data Augmentation

Why Learn Computer Vision in Python using OpenCV?

Computer vision applications and technology are exploding right now! With several apps and industries making amazing use of the technology, from billion dollar apps such as Pokémon GO, Snapchat and up and coming apps like MSQRD and PRISMA.

Even Facebook, Google, Microsoft, Apple, Amazon, and Tesla are all heavily utilizing computer vision for face & object recognition, image searching and especially in Self-Driving Cars!

As a result, the demand for computer vision expertise is growing exponentially!

However, learning computer vision is hard! Existing online tutorials, textbooks, and free MOOCs are often outdated, using older an incompatible libraries or are too theoretical, making it difficult to understand.

This was my problem when learning Computer Vision and it became incredibly frustrating. Even simply running example code I found online proved difficult as libraries and functions were often outdated.

I created this course to teach you all the key concepts without the heavy mathematical theory while using the most up to date methods.

I take a very practical approach, using more than 50 Code Examples.

At the end of the course, you will be able to build 12 Awesome Computer Vision Apps using OpenCV in Python.

I use OpenCV which is the most well supported open source computer vision library that exists today! Using it in Python is just fantastic as Python allows us to focus on the problem at hand without being bogged down by complex code.

If you're an academic or college student I still point you in the right direction if you wish to learn more by linking the research papers of techniques we use.

So if you want to get an excellent foundation in Computer Vision, look no further.

This is the course for you!

In this course, you will discover the power of OpenCV in Python, and obtain skills to dramatically increase your career prospects as a Computer Vision developer.

You will learn:

The key concepts of Computer Vision & OpenCV.

To perform image manipulations such as transformations, cropping, blurring, thresholding, edge detection and cropping.

To segment images by understanding contours, circle, and line detection. You'll even learn how to approximate contours, do contour filtering and ordering as well as approximations.

Use feature detection (SIFT, SURF, FAST, BRIEF & ORB) to do object detection.

Implement Object Detection for faces, people & cars.

Extract facial landmarks for face analysis, applying filters and face swaps.

Implement Machine Learning in Computer Vision for handwritten digit recognition.

Implement Facial Recognition.

Implement and understand Motion Analysis & Object Tracking.

Use basic computational photography techniques for Photo Restoration (eliminate marks, lines, creases, and smudges from old damaged photos).

How to become a true computer vision expert by getting started in Deep Learning

12 Cool computer vision startup ideas

As for Updates and support:

I will be continuously adding updates, fixes, and new amazing projects every month!

I will be active daily in the 'questions and answers' area of the course, so you are never on your own.

So, are you ready to get started? Enroll now and start the process of becoming a master in Computer Vision today!

Who this course is for:

Beginners who have an interest in computer vision
College students looking to get a head start before starting computer vision research
Anyone curious using Deep Learning for Computer Vision
Entrepreneurs looking to implement computer vision startup ideas
Hobbyists wanting to make a cool computer vision prototype
Software Developers and Engineers wanting to develop a computer vision skillset