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Building Features from Image Data

by Janani Ravi

This course covers conceptual and practical aspects of pre-processing images to maximize the efficacy of image processing algorithms, as well as implementing feature extraction, dimensionality reduction, and latent factor identification.

What you'll learn

From machine-generated art to visualizations of black holes, some of the hottest applications of ML and AI these days are to data in image form.

In this course, Building Features from Image Data, you will gain the ability to structure image data in a manner ideal for use in ML models.

First, you will learn how to pre-process images using operations such as making the aspect ratio uniform, normalizing pixel magnitudes, and cropping images to be square in shape. Next, you will discover how to implement denoising techniques such as ZCA whitening and batch normalization to remove variations.

Finally, you will explore how to identify points and blobs of interest and calculate image descriptors using algorithms such as Histogram of Oriented Gradients and Scale Invariant Feature Transform.

You will round out the course by implementing dimensionality reduction using dictionary learning, feature extraction using convolutional kernels, and latent factor identification using autoencoders.

When you’re finished with this course, you will have the skills and knowledge to move on to pre-process images in conceptually and practically sound ways to extract features from such data for use in machine learning models.

About the author

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more

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