Build Sarah – An Image Classification AI
$50 $15
Overview
This title is part of the Python Mini-Degree – 12 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 12 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.
Learn more and enroll HERE.
Curriculum
Course Introduction M1-1 Intro to Machine Learning M1-2 Supervised Classification M1-3 Image Classification M1-4 Nearest Neighbor Classifier M1-5 k Nearest Neighbors Classifier M1-6 Hyperparameters and Cross Validation M2-1 CIFAR10 Dataset M2-2 Intro to Provided Code M2-3 Nearest Neighbor 1 M2-4 Nearest Neighbor 2 M2-5 Nearest Neighbor 3 M2-6 k Nearest Neighbors M2-7 Hyperparameter Tuning ConclusionRequirements
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.
