Building Features for Computer Vision in Microsoft Azure from Pluralsight

This course will cover how to leverage both an algorithmic as well as a deep learning approach for building features from image data on Microsoft Azure.

Computer vision enables insights and experiences that previously weren’t possible, but it can seem daunting to know how to extract the information you need out of an image. In this course, Building Features from Image Data in Microsoft Azure, you will learn how to leverage the tools and services provided by Microsoft Azure alongside popular computer vision and deep learning frameworks to extract relevant information from images. First, you will explore computer vision, its use cases, and also take a look at what Azure provides to make this easier for you. Next, you will learn about the algorithmic approach to computer vision by reviewing popular feature descriptors like the scale-invariant feature transform and the histogram of oriented gradients. Finally, you will delve into deep learning as a tool to leverage in computer vision by creating a convolutional neural network to classify images. When you are finished with this course, you will have both the knowledge and tools to build features out of your image data on Microsoft Azure.

What's inside

Syllabus

Course Overview

Exploring Computer Vision on Azure

Utilizing the SIFT and HOG Algorithms for Feature Detection

Leveraging Convolutional Neural Networks for Feature Extraction

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops algorithmic and deep learning computer vision skills, which are in high demand across industries as diverse as healthcare and marketing

Strong focus on Microsoft Azure tools and services, which are widely used in industry

Taught by David Tucker, who has extensive experience in computer vision and deep learning

Provides a practical approach to building features from image data using both traditional and deep learning techniques

Suitable for learners with a background in computer science or a related field

Assumes familiarity with popular computer vision and deep learning frameworks

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 for Computer Vision in Microsoft Azure with these activities:

Reach out to experts in computer vision and machine learning

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Seek guidance and support from experienced professionals in the field to gain valuable insights and enhance your learning journey.

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Identify potential mentors on platforms like LinkedIn
Craft a personalized message expressing interest in mentorship
Follow up and schedule a meeting or call

Offer mentorship to junior learners in computer vision

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Enhance your knowledge by sharing it with others and providing guidance to junior learners in computer vision, reinforcing your understanding and fostering a supportive learning community.

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Identify platforms or communities where you can connect with junior learners
Offer your help and support
Provide guidance and feedback on their learning journey
Encourage their growth and development

Review computer vision concepts

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Refresh your knowledge in feature detection techniques used in computer vision to build a stronger foundation for this course.

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Review the concept of feature detection
Explore different feature detectors, such as SIFT and HOG
Understand the strengths and weaknesses of each feature detector

Six other activities

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Write a blog post summarizing the key concepts of computer vision

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Solidify your understanding by creating content that explains the key concepts of computer vision, reinforcing your learning and potentially benefiting others.

Browse courses on Computer Vision

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Outline the key concepts of computer vision
Write the blog post in clear and concise language
Proofread and edit the post
Publish the post on a relevant platform

Follow tutorials on deep learning for image classification

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Deepen your understanding of deep learning techniques used for image classification, particularly using convolutional neural networks.

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Find tutorials on deep learning for image classification
Follow the tutorials step-by-step
Implement the deep learning models you learn

Participate in open-source projects related to computer vision

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Contribute to the community by participating in open-source projects related to computer vision, gaining real-world experience and expanding your knowledge.

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Find open-source projects on platforms like GitHub
Identify areas where you can contribute
Submit pull requests with your contributions
Collaborate with other contributors

Practice image feature extraction using Azure Machine Learning

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Enhance your practical skills by working through exercises that involve extracting image features using Azure Machine Learning.

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Set up an Azure Machine Learning environment
Load and preprocess image data
Apply different feature extraction techniques to the images
Evaluate the performance of the extracted features

Build a simple image classification model using Azure Machine Learning

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Apply your learning by creating a simple image classification model using Azure Machine Learning, which will reinforce your understanding of the concepts covered in this course.

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Gather a dataset of images for classification
Build an image classification model using Azure Machine Learning
Train and evaluate the model
Deploy the model and make predictions

Develop an image processing pipeline for a specific domain

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Challenge yourself by undertaking a project that involves developing a complete image processing pipeline for a specific domain, which will provide a comprehensive hands-on experience.

Browse courses on Computer Vision

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Identify a specific domain for the image processing pipeline
Gather and preprocess a dataset of images
Design and implement the image processing pipeline
Evaluate the performance of the pipeline
Package and deploy the pipeline

Career center

Learners who complete Building Features for Computer Vision in Microsoft Azure will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers specialize in the design and development of computer algorithms that can learn and solve complex problems without explicit instructions from humans. In this course, you will learn how to leverage both an algorithmic as well as a deep learning approach for building features from image data on Microsoft Azure.

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Researcher

Researchers conduct original investigations and experiments to contribute new knowledge to a field of study. This course can be useful for researchers who want to learn more about computer vision and how to use it to solve problems.

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Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data to help businesses make informed decisions. This course can be useful for building a foundation in computer vision, both from an algorithmic and deep learning approach, which are useful for extracting relevant information from images.

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Computer Vision Engineer

A Computer Vision Engineer will work on designing, developing, and testing hardware and software computer vision systems for use in various applications such as robotics, medical imaging, and autonomous vehicles. This course can be helpful for building a foundation in computer vision to detect objects based on image data for engineering computer vision systems.

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Project Manager

Project Managers plan, execute, and close projects. This course can be helpful for managing projects that involve computer vision, as it covers the basics of computer vision and how to extract relevant information from images.

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Data Analyst

Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course can help build a foundation in computer vision to analyze data using image data.

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Consultant

Consultants provide advice and expertise to businesses and individuals. This course can be useful for consultants who want to learn more about computer vision and how to use it to help their clients.

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Entrepreneur

Entrepreneurs start and run their own businesses. This course can be useful for entrepreneurs who want to learn more about computer vision and how to use it to develop new products or services.

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Technical Writer

Technical Writers create documentation for computer software and applications. This course can be useful for learning how to communicate complex technical information about computer vision to a non-technical audience.

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Salesperson

Salespeople sell products and services to businesses and individuals. This course can be useful for salespeople who want to learn more about computer vision and how it can be used to improve sales.

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Teacher

Teachers instruct students at all levels of education. This course can be helpful for teachers who want to incorporate computer vision into their lessons.

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Software Engineer

Software Engineers design, develop, and test computer software and applications. This course may be useful for those interested in building features from image data, as it covers both algorithmic and deep learning approaches for extracting relevant information from images.

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Computer Programmer

Computer Programmers write and maintain the code that powers computer applications and software. This course may be useful for those interested in building features from image data, as it covers both algorithmic and deep learning approaches for extracting relevant information from images.

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Product Manager

Product Managers are responsible for the development and launch of new products or services. This course can be useful for understanding how computer vision can be used to improve products or services.

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Business Analyst

Business Analysts bridge the gap between business and technology by analyzing business needs and recommending technology solutions. This course can be useful for understanding how computer vision can be applied to solve business problems.

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