Machines-learning-image-clasification-with-Python

Build Sarah – An Image Classification AI

$50

$50
Includes:
  • 16 Lessons
  • Lifetime, 24/7 Access
  • Certificate of Completion
  • English Subtitles
Join 300,000+ Satisfied Students
$50

Overview

This title is part of the Python Mini-Degree – 13 Courses to Learn and Master Python

In this course we’ll be building an image classification Artificial Intelligence (AI) that can detect transportation vehicles, animals and other objects. We’ll train our AI with a dataset of images, and then give it new images which it will be able to classify.

We’ll begin by introducing the concept of image classification and Machine Learning. We’ll the cover the Nearest Neighbor Classifier technique and write a script to implement it. Then, we’ll move on to covering the k-Nearest Neighbor technique which provides a more generic approach to solving this problem. Parameters of our AI will be discussed and we’ll use Hyperparameter Tuning for that. To train our image we’ll use the CIFAR-10 Image Classification Dataset, which is broadly used in academia and the industry.

About the Python Mini-Degree

The Python Mini-Degree is a bundle of 13 online video courses, that go all the way from teaching you how to code in Python while making a game, to building your own Artificial Intelligence (AI) and Internet of Things (IoT) applications using Computer Vision, Machine Learning and Deep Learning.

https://www.youtube.com/watch?v=oYGVCPt22Cs

Learn more and enroll HERE.

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What Our Members Are Saying

“I was able to just continuously expand upon the groundwork that I created with the course and turn it into something that was uniquely mine.”
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Curriculum

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Requirements

    This course builds up on the concepts covered in our course Create a Raspberri Pi Smart Security Camera.

    You need to have Python 2.7 installed.

    It is recommended to take this course as part of the Python Mini-Degree, which includes 12 courses on Python, Computer Vision, Machine Learning, Deep Learning and much more.

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