Building Features from Image Data

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.
Course info
Level
Advanced
Updated
Aug 13, 2019
Duration
2h 10m
Table of contents
Description
Course info
Level
Advanced
Updated
Aug 13, 2019
Duration
2h 10m
Description

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
About the author

A problem solver at heart, 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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
[Autogenerated] Hi, My name is Johnny Ravi and welcome to the school's on building features from image data. A little about myself. I have a master's degree in electrical engineering from Stanford and about that, companies such as Microsoft, Google and Flip Cards at Google was one of the first engineers working on a real time collaborative editing in Google Dogs and I hold four patterns for it online technologies. I currently work on my own stuff up Loony Con, a studio for high quality video content from machine generated art. Visualizations off black holds some of the hottest applications off M L and E I. D stays are toe data in image form. In this course, you will gain the ability to structure image data in a manner ideal for use and ML motives. First, you learn how to be process images using operations such as making the aspect issue uniform normalizing pixel magnitudes on cropping images to be square and shape. Next, you will discover how to implement Denoix zing techniques such as zc whitening on batch normalization, toe removed variations. Finally, you will explore how to identify points on blobs, off interest and calculate image descriptors using algorithms such as history graham off audience ingredients on a scale in varying feature transform. You will round out the course by implementing dimensionality reduction, using actually learning feature extraction using convolution, colonels and late and factor identification using auto in quarters. When you're finished with this course, you will have the skills and knowledge to wantto reprocess images in conceptually and practically found these on extract teachers from such data for using machine learning markets.