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Machine Learning for Kyphosis Disease Classification
Ryan Ahmed
Ryan Ahmed
The objective of this project is to predict whether a patient has kyphosis or not, based on given features and diagnostic measurements such as age and number of vertebrae. Kyphosis is an abnormally excessive convex curvature of the spine. This guided project is practical and directly applicable to the healthcare industry. You can add this project to your portfolio of projects which is essential for your next job interview.
Register for this course and see more details by visiting:
OpenCourser.com/course/0hd255/machine
What's inside
Syllabus
Project Overview
The objective of this project is to predict whether a patient has kyphosis or not, based on given features and diagnostic measurements such as age and number of vertebrae. Kyphosis is an abnormally excessive convex curvature of the spine.
This guided project is practical and directly applicable to the healthcare industry. You can add this project to your portfolio of projects which is essential for your next job interview.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
In-demand, highly relevant in the healthcare industry
Provides practical, applicable skills for the healthcare industry
Enhances portfolio for job interviews
Suitable for learners with a background in healthcare
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Activities
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We're preparing activities for
Machine Learning for Kyphosis Disease Classification.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Machine Learning for Kyphosis Disease Classification will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. Machine learning for kyphosis disease classification is a subfield of data science that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and implement machine learning algorithms to solve a variety of problems. Machine learning for kyphosis disease classification is a subfield of machine learning that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. Machine learning for kyphosis disease classification is a subfield of statistics that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Machine learning for kyphosis disease classification is a subfield of software engineering that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Data Analyst
Data Analyst
Data Analysts work with data to identify trends and patterns that can be used to make informed decisions. Machine learning for kyphosis disease classification is a subfield of data analysis that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this practical field and can help you build a strong foundation.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to develop and implement financial models. Machine learning for kyphosis disease classification is a subfield of quantitative analysis that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers use their knowledge of engineering to advance human health. Machine learning for kyphosis disease classification is a subfield of biomedical engineering that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Teacher
Teacher
Teachers educate students in a variety of subjects. Machine learning for kyphosis disease classification is a subfield of education that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Researcher
Researcher
Researchers conduct scientific studies to advance knowledge. Machine learning for kyphosis disease classification is a subfield of research that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Physicist
Physicist
Physicists study the fundamental laws of nature. Machine learning for kyphosis disease classification is a subfield of physics that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts use their knowledge of data analysis to improve the quality and efficiency of healthcare delivery. Machine learning for kyphosis disease classification is a subfield of healthcare data analysis that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Zoologist
Zoologist
Zoologists study animals and their behavior. Machine learning for kyphosis disease classification is a subfield of zoology that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field can help you build a strong foundation.
Radiologist
Radiologist
Radiologists use medical imaging to diagnose and treat diseases. Machine learning for kyphosis disease classification is a subfield of radiology that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field can help you build a strong foundation.
Health Informatics Specialist
Health Informatics Specialist
Health Informatics Specialists use their knowledge of information technology to improve the efficiency and effectiveness of healthcare delivery. Machine learning for kyphosis disease classification is a subfield of health informatics that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
Orthopedic Surgeon
Orthopedic Surgeon
Orthopedic Surgeons diagnose and treat disorders of the musculoskeletal system, including the spine. Machine learning for kyphosis disease classification is a subfield of orthopedic surgery that deals with developing algorithms to identify and classify diseases of the spine. Taking this course will give you practical experience in applying machine learning to this field and can help you build a strong foundation.
For more career information including salaries, visit:
OpenCourser.com/course/0hd255/machine
Reading list
We've selected 14 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
Machine Learning for Kyphosis Disease Classification.
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Provides a comprehensive overview of deep learning, which subfield of machine learning that is particularly useful for tasks such as image recognition.
Provides a comprehensive overview of generative adversarial networks, which subfield of machine learning that is particularly useful for tasks such as image generation.
Provides a comprehensive overview of machine learning with Python, which is helpful for those who want to use Python for their machine learning projects.
Provides an overview of machine learning techniques commonly used in healthcare.
Provides a comprehensive overview of reinforcement learning, which subfield of machine learning that is particularly useful for tasks such as game playing.
Provides a theoretical and practical overview of machine learning in medicine.
Provides a practical guide to using Python for data analysis, which is essential for machine learning.
Provides a comprehensive overview of medical image analysis techniques. It would be useful for students who want to learn more about how machine learning is used to analyze medical images.
Provides a comprehensive overview of statistical methods used in machine learning. It would be useful for students who want to learn more about the statistical foundations of machine learning.
Provides a practical guide to using R for machine learning, which is helpful for those who want to use R for their machine learning projects.
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Provides a foundational understanding of biostatistics, which is essential for understanding medical research.
Provides a comprehensive overview of medical terminology, which is essential for understanding medical concepts.
Provides a gentle introduction to machine learning, which is helpful for those new to the field.
Provides a gentle introduction to machine learning for beginners. It would be useful for students who have no prior knowledge of machine learning and want to learn the basics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0hd255/machine
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
In-demand, highly relevant in the healthcare industry
Provides practical, applicable skills for the healthcare industry
Enhances portfolio for job interviews
Suitable for learners with a background in healthcare
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