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This is a self-paced lab that takes place in the Google Cloud console. Deploy and test a visual inspection AI component anomaly detection solution.
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What's inside
Syllabus
Deploy and Test a Visual Inspection AI Component Anomaly Detection Solution
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Ideal for learners with a background and interest in visual inspection AI component anomaly detection solutions
Taught by Google Cloud Training, a widely recognized source of expertise in this field
Save this course
Save Deploy and Test a Visual Inspection AI Component Anomaly Detection Solution to your list so you can find it easily later:
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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 Deploy and Test a Visual Inspection AI Component Anomaly Detection Solution with these
activities:
Review Python Basics
Show steps
Get a refresher on Python's core concepts and syntax before starting the course to ensure a solid foundation.
Browse courses on
Python
Show steps
-
Go over Python's data types, operators, and control flow.
-
Write simple Python scripts to practice basic operations.
Solve Anomaly Detection Practice Problems
Show steps
Reinforce understanding of anomaly detection concepts by solving a variety of practice problems and exercises.
Show steps
-
Find online resources or textbooks with anomaly detection practice problems.
-
Solve the problems and check your solutions against provided answers.
Build a Simple Visual Inspection AI Project
Show steps
Develop a hands-on project that applies the concepts learned in the course by building a basic visual inspection AI solution.
Show steps
-
Choose a simple visual inspection task, such as identifying objects in images.
-
Collect and prepare a dataset of images for the task.
-
Train a basic visual inspection AI model using the dataset.
-
Deploy the trained model and evaluate its performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Present an Anomaly Detection Solution
Show steps
Develop a deep understanding of anomaly detection by creating a comprehensive presentation that explains and demonstrates a specific solution.
Show steps
-
Choose a specific anomaly detection problem to focus on.
-
Research and gather information on the problem and potential solutions.
-
Design and implement an anomaly detection solution.
-
Prepare a presentation to explain the problem, solution, and results.
Explore Advanced Anomaly Detection Techniques
Show steps
Explore more advanced anomaly detection techniques and tools beyond the scope of the course to expand knowledge and skills.
Show steps
-
Research and identify advanced anomaly detection algorithms.
-
Follow tutorials or online courses to learn about these techniques.
-
Apply the techniques to real-world datasets to gain practical experience.
Contribute to Open-Source Anomaly Detection Projects
Show steps
Gain practical experience and contribute to the community by participating in open-source projects related to anomaly detection.
Show steps
-
Identify open-source anomaly detection projects or initiatives.
-
Explore the project codebase and identify areas to contribute.
-
Make code contributions, submit bug reports, or participate in discussions.
Participate in Anomaly Detection Challenges
Show steps
Challenge yourself and test your skills by participating in anomaly detection competitions or hackathons.
Show steps
-
Find and register for relevant anomaly detection challenges.
-
Form a team or work individually to solve the challenge problem.
-
Submit your solution and compete for recognition or prizes.
Review Python Basics
Show steps
Get a refresher on Python's core concepts and syntax before starting the course to ensure a solid foundation.
Browse courses on
Python
Show steps
Show steps
Show steps
- Go over Python's data types, operators, and control flow.
- Write simple Python scripts to practice basic operations.
Solve Anomaly Detection Practice Problems
Show steps
Reinforce understanding of anomaly detection concepts by solving a variety of practice problems and exercises.
Show steps
Show steps
Show steps
- Find online resources or textbooks with anomaly detection practice problems.
- Solve the problems and check your solutions against provided answers.
Build a Simple Visual Inspection AI Project
Show steps
Develop a hands-on project that applies the concepts learned in the course by building a basic visual inspection AI solution.
Show steps
Show steps
Show steps
- Choose a simple visual inspection task, such as identifying objects in images.
- Collect and prepare a dataset of images for the task.
- Train a basic visual inspection AI model using the dataset.
- Deploy the trained model and evaluate its performance.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Present an Anomaly Detection Solution
Show steps
Develop a deep understanding of anomaly detection by creating a comprehensive presentation that explains and demonstrates a specific solution.
Show steps
Show steps
Show steps
- Choose a specific anomaly detection problem to focus on.
- Research and gather information on the problem and potential solutions.
- Design and implement an anomaly detection solution.
- Prepare a presentation to explain the problem, solution, and results.
Explore Advanced Anomaly Detection Techniques
Show steps
Explore more advanced anomaly detection techniques and tools beyond the scope of the course to expand knowledge and skills.
Show steps
Show steps
Show steps
- Research and identify advanced anomaly detection algorithms.
- Follow tutorials or online courses to learn about these techniques.
- Apply the techniques to real-world datasets to gain practical experience.
Contribute to Open-Source Anomaly Detection Projects
Show steps
Gain practical experience and contribute to the community by participating in open-source projects related to anomaly detection.
Show steps
Show steps
Show steps
- Identify open-source anomaly detection projects or initiatives.
- Explore the project codebase and identify areas to contribute.
- Make code contributions, submit bug reports, or participate in discussions.
Participate in Anomaly Detection Challenges
Show steps
Challenge yourself and test your skills by participating in anomaly detection competitions or hackathons.
Show steps
Show steps
Show steps
- Find and register for relevant anomaly detection challenges.
- Form a team or work individually to solve the challenge problem.
- Submit your solution and compete for recognition or prizes.
Career center
Learners who complete Deploy and Test a Visual Inspection AI Component Anomaly Detection Solution will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Developing computer vision models is one of the core functions of a Machine Learning Engineer, who may specialize in computer vision, visual AI, deep learning, image processing, or data science. This course may be particularly relevant to Machine Learning Engineers specializing in visual inspection AI who wish to extend their technical capabilities.
Data Scientist
Data Scientist
The skills learned in this course align with much of the work performed by Data Scientists who specialize in image analysis, such as image recognition, object tracking, object detection, image segmentation, etc. This course may be particularly relevant to Data Scientists who wish to learn how to build and operate production-ready machine learning pipelines.
Software Engineer
Software Engineer
Software Engineers who work with computer vision may find this course particularly helpful as it provides practical experience deploying and testing anomaly detection AI components. This course may help build a foundation for Software Engineers who wish to work on visual inspection and anomaly detection systems.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers working with anomaly detection may find this course useful as it provides hands-on experience building a complete anomaly detection AI solution. This course may help develop skills in deploying and testing AI solutions for real-world use cases, which could be a valuable asset for Computer Vision Engineers.
Data Analyst
Data Analyst
Data Analysts working in fields such as image or video analysis may find this course helpful as it could provide insight into building and deploying anomaly detection solutions for processing large volumes of data. This course may help Data Analysts build a foundation in applying visual inspection AI techniques, which can be useful for understanding the capabilities and limitations of these methods.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance roles may require an understanding of AI/ML testing to ensure the quality of products. This course may be useful for Quality Assurance Analysts who wish to build a foundation in testing an anomaly detection AI component or system.
Product Manager
Product Manager
Product Managers for products such as visual inspection systems may find this course helpful for understanding aspects of building and deploying anomaly detection AI components. This course can help provide Product Managers with technical knowledge that will assist them in making better decisions and communicating with their teams.
Business Analyst
Business Analyst
Business Analysts working with companies utilizing AI solutions may find this course helpful for understanding the process of building and testing an anomaly detection AI component. This course may help provide Business Analysts with technical knowledge that can assist them in evaluating the feasibility and impact of proposed AI solutions.
Project Manager
Project Manager
Project Managers who work with AI/ML teams may find this course helpful for understanding the process of building and testing an anomaly detection AI component. This course may help Project Managers gain technical knowledge that will assist them in managing AI/ML projects more effectively.
AI Engineer
AI Engineer
AI Engineers working on anomaly detection may find this course useful as it provides hands-on experience building and testing an anomaly detection AI solution. This course may help AI Engineers build a foundation in deploying and testing AI solutions for real-world use cases.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts may find this course useful for understanding the process of building and testing an anomaly detection AI component. This course may help Operations Research Analysts gain technical knowledge that can assist them in optimizing processes involving AI/ML systems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts may find this course helpful for understanding the process of building and testing an anomaly detection AI component. This course may help Quantitative Analysts gain technical knowledge that can assist them in building and evaluating AI/ML models for financial analysis.
Solutions Architect
Solutions Architect
Solutions Architects who work on AI/ML projects may find this course helpful for understanding the process of building and testing an anomaly detection AI component. This course may help Solutions Architects gain technical knowledge that will assist them in designing and implementing AI/ML solutions.
Risk Analyst
Risk Analyst
Risk Analysts may find this course helpful for understanding the process of building and testing an anomaly detection AI component. This course may help Risk Analysts gain technical knowledge that can assist them in identifying and mitigating risks associated with AI/ML systems.
Statistician
Statistician
Statisticians may find this course helpful for understanding the process of building and testing an anomaly detection AI component. This course may help Statisticians gain technical knowledge that can assist them in applying statistical methods to AI/ML systems.
For more career information including salaries, visit:
OpenCourser.com/course/1lyk0l/deploy
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Deploy and Test a Visual Inspection AI Component Anomaly Detection Solution.
Provides a comprehensive overview of machine learning algorithms for computer vision. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. This book valuable resource for anyone who wants to learn more about the machine learning algorithms used in computer vision.
Provides a comprehensive overview of data mining and knowledge discovery. It covers topics such as data preprocessing, feature selection, classification, and clustering. This book valuable resource for anyone who wants to learn more about data mining and knowledge discovery.
This advanced textbook covers a wide range of machine learning topics, including pattern recognition and anomaly detection, providing in-depth theoretical knowledge.
Provides a comprehensive overview of TensorFlow for deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. This book valuable resource for anyone who wants to learn more about TensorFlow for deep learning.
Provides a comprehensive overview of anomaly detection algorithms. It covers topics such as statistical methods, machine learning methods, and deep learning methods. This book valuable resource for anyone who wants to learn more about anomaly detection algorithms.
Provides a comprehensive overview of outlier analysis. It covers topics such as statistical methods, machine learning methods, and deep learning methods. This book valuable resource for anyone who wants to learn more about outlier analysis.
Focuses on deep learning techniques for computer vision tasks, providing insights into the use of neural networks for anomaly detection in images.
Provides a comprehensive overview of Keras with Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. This book valuable resource for anyone who wants to learn more about Keras with Python.
This comprehensive book covers a wide range of machine learning topics, including anomaly detection, and is widely used as a textbook.
Provides a comprehensive overview of the fundamental concepts and techniques in image processing and computer vision. It covers topics such as image acquisition, image processing, feature extraction, and object recognition. This book valuable resource for anyone who wants to learn more about the basics of image processing and computer vision.
Offers practical guidance on implementing anomaly detection algorithms in Python, complementing the hands-on approach of the course.
Save
This comprehensive textbook offers a foundation in computer vision algorithms and techniques, providing a broader understanding of the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1lyk0l/deploy
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Effort
90 minutes
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Ideal for learners with a background and interest in visual inspection AI component anomaly detection solutions
Taught by Google Cloud Training, a widely recognized source of expertise in this field
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