Building Features from Image Data from Pluralsight

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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.

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.

What's inside

Syllabus

Course Overview

Representing Images as Features for Machine Learning

Detecting Features and Text in Images

Simplifying Image Processing Using Dimensionality Reduction

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what should give you pause

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Provides conceptual and practical approaches to image preprocessing, feature extraction, and latent factor identification

Covers techniques such as ZCA whitening, batch normalization, Histogram of Oriented Gradients, and autoencoders, which are widely used in industry

Suitable for learners seeking to build a strong foundation in feature engineering for machine learning models

Reviews summary

Image feature engineering for ml

According to learners, this course offers a comprehensive and practical approach to building features from image data. Students find it highly effective for learning how to pre-process images, implement denoising techniques, and extract features using algorithms like HOG and SIFT. The curriculum also introduces more advanced topics such as dimensionality reduction via dictionary learning and latent factor identification with autoencoders. While largely positively received for its breadth and practical focus, some suggest that learners benefit from having prior experience in machine learning and mathematics to fully grasp the material.

Explores advanced topics like autoencoders and conv kernels.

"The introduction to convolutional kernels and autoencoders made the course highly relevant."

"I valued the exploration of latent factor identification, a key modern ML topic."

"The course provides a good overview of contemporary techniques for image feature engineering."

Equips learners to prepare image data for ML models.

"I appreciate how the course directly shows how to structure image data for machine learning models."

"The focus on practical implementation of feature extraction for ML was extremely beneficial."

"I can now confidently apply these image data preparation techniques in my own ML projects."

Covers essential image pre-processing & feature engineering.

"I found the course comprehensive, covering crucial image pre-processing steps effectively."

"It introduces a wide array of techniques from denoising to feature extraction like HOG and SIFT."

"The modules on dimensionality reduction and latent factor identification were particularly insightful."

Learners should have foundational understanding of ML and math.

"I recommend having a solid understanding of machine learning basics and linear algebra before starting."

"This course is better suited for those with some prior experience in Python and data science."

"Learners without a strong mathematical background might find some concepts challenging."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Building Features from Image Data with these activities:

Review basic probability and statistics

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Knowing probability and statistics will allow you to leverage average values, distributions, and calculate significant values to help understand your results better.

Browse courses on Probability and Statistics

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Review a textbook or online resources on probability and statistics.
Complete practice problems related to probability and statistics
Apply probability and statistics concepts to real-world examples

Learn about dimensionality reduction techniques

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Dimensionality reduction techniques can reduce the complexity of an image while preserving important information. They can improve the efficiency and accuracy of image processing algorithms.

Browse courses on Dimensionality Reduction

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Find tutorials on dimensionality reduction techniques specific to image processing.
Complete the tutorials and implement the techniques in your own projects.
Compare the results of using different dimensionality reduction techniques.

Practice implementing feature extraction algorithms

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Feature extraction algorithms can extract meaningful features from images, which can improve the performance of machine learning models. Practicing these algorithms will help you develop proficiency in feature engineering.

Browse courses on Feature Extraction

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Find datasets with images.
Implement different feature extraction algorithms, such as HOG, SIFT, and SURF.
Evaluate the performance of the extracted features using machine learning models.

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Contribute to an open-source image processing library

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Contributing to an open-source image processing library will allow you to work on real-world projects, collaborate with others, and gain valuable experience.

Browse courses on Open Source

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Find an open-source image processing library that aligns with your interests.
Identify an area where you can make a contribution.
Implement your contribution and submit a pull request.

Career center

Learners who complete Building Features from Image Data will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist gathers, analyzes, and interprets data to extract meaningful insights. Building Features from Image Data can serve as a helpful complement to a Data Scientist's skillset. This course provides a solid foundation in extracting features from image data, a skill that is becoming increasingly important in a variety of industries.

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Computer Vision Engineer

A Computer Vision Engineer designs and develops computer vision systems that can interpret and understand images. Building Features from Image Data may be helpful for a Computer Vision Engineer who needs to extract features from images for use in machine learning models.

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Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. Building Features from Image Data can be useful for a Machine Learning Engineer who needs to build models that use image data.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. Building Features from Image Data can be useful for a Software Engineer who needs to develop applications that use image data.

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Data Analyst

A Data Analyst collects, analyzes, and interprets data to provide insights to businesses. Building Features from Image Data may be useful for a Data Analyst who needs to extract features from image data for use in data analysis.

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Business Analyst

A Business Analyst analyzes business processes and data to identify opportunities for improvement. Building Features from Image Data may be useful for a Business Analyst who needs to extract features from image data for use in business analysis.

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Product Manager

A Product Manager develops and manages products. Building Features from Image Data may be useful for a Product Manager who needs to understand how to extract features from image data for use in product development.

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Project Manager

A Project Manager plans, executes, and closes projects. Building Features from Image Data may be useful for a Project Manager who needs to understand how to extract features from image data for use in project management.

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Marketing Manager

A Marketing Manager plans and executes marketing campaigns. Building Features from Image Data may be useful for a Marketing Manager who needs to understand how to extract features from image data for use in marketing campaigns.

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Sales Manager

A Sales Manager leads and manages a sales team. Building Features from Image Data may be useful for a Sales Manager who needs to understand how to extract features from image data for use in sales presentations.

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Customer Success Manager

A Customer Success Manager helps customers achieve success with a product or service. Building Features from Image Data may be useful for a Customer Success Manager who needs to understand how to extract features from image data for use in customer support.

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Technical Writer

A Technical Writer creates and maintains technical documentation. Building Features from Image Data may be useful for a Technical Writer who needs to understand how to extract features from image data for use in technical documentation.

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UX Designer

A UX Designer designs user interfaces for websites and applications. Building Features from Image Data may be useful for a UX Designer who needs to understand how to extract features from image data for use in UX design.

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Graphic Designer

A Graphic Designer creates visual concepts, using computer software or by hand, to communicate ideas that inspire, inform, and captivate consumers.

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Photographer

A Photographer captures visual images using a camera. They may specialize in a particular type of photography, such as portraiture, landscape, or photojournalism.

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