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Ryan Ahmed
In this project-based course, we will build, train and test a machine learning model to detect diabetes with XG-boost and Artificial Neural Networks. The objective of this project is to predict whether a patient has diabetes or not based on their given features and diagnostic measurements such as number of pregnancies, insulin levels, Body mass index, age and blood pressure.
Register for this course and see more details by visiting:
OpenCourser.com/course/0cnze7/diabetes
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What's inside
Syllabus
Project Overview
In this project-based course, we will build, train and test a machine learning model to detect diabetes with XG-boost and Artificial Neural Networks. The objective of this project is to predict whether a patient has diabetes or not based on their given features and diagnostic measurements such as number of pregnancies, insulin levels, Body mass index, age and blood pressure.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills that are highly relevant to industry
Strong fit for learners who are entering or early in this field
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Save Diabetes Disease Detection with XG-Boost and Neural Networks 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 Diabetes Disease Detection with XG-Boost and Neural Networks with these
activities:
Review fundamentals of machine learning and artificial neural networks
Show steps
Strengthen foundational knowledge to enhance understanding of the core concepts covered in the course.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Review course materials and textbooks
-
Explore online resources and tutorials
-
Attend refresher sessions or workshops
Organize and compile study materials
Show steps
Create a centralized repository of resources to enhance accessibility and improve learning efficiency.
Show steps
-
Gather and download lecture notes, slides, and assignments
-
Create a structured filing system for easy retrieval
-
Review materials regularly to reinforce understanding
Project: Implement a diabetes diagnostic tool
Show steps
Build a diabetes detection tool to apply the concepts and techniques learned in the course.
Show steps
-
Gather and clean data
-
Train and evaluate the model
-
Deploy the model into a user interface
Two other activities
Expand to see all activities and additional details
Show all five activities
Attend a workshop on advanced machine learning techniques
Show steps
Gain practical knowledge and insights by attending expert-led workshops on machine learning.
Show steps
-
Research and identify relevant workshops
-
Register and attend the workshop
-
Apply the techniques learned to your diabetes detection project
Create a blog post or article on diabetes detection techniques
Show steps
Demonstrate understanding of diabetes detection by creating a detailed resource for others.
Browse courses on
Machine Learning Applications
Show steps
-
Research and gather information
-
Write and edit the content
-
Publish and promote the content
Review fundamentals of machine learning and artificial neural networks
Show steps
Strengthen foundational knowledge to enhance understanding of the core concepts covered in the course.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Review course materials and textbooks
- Explore online resources and tutorials
- Attend refresher sessions or workshops
Organize and compile study materials
Show steps
Create a centralized repository of resources to enhance accessibility and improve learning efficiency.
Show steps
Show steps
Show steps
- Gather and download lecture notes, slides, and assignments
- Create a structured filing system for easy retrieval
- Review materials regularly to reinforce understanding
Project: Implement a diabetes diagnostic tool
Show steps
Build a diabetes detection tool to apply the concepts and techniques learned in the course.
Show steps
Show steps
Show steps
- Gather and clean data
- Train and evaluate the model
- Deploy the model into a user interface
Two other activities
Expand to see all activities and additional details
Show all five activities
Attend a workshop on advanced machine learning techniques
Show steps
Gain practical knowledge and insights by attending expert-led workshops on machine learning.
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Register and attend the workshop
- Apply the techniques learned to your diabetes detection project
Create a blog post or article on diabetes detection techniques
Show steps
Demonstrate understanding of diabetes detection by creating a detailed resource for others.
Browse courses on
Machine Learning Applications
Show steps
Show steps
Show steps
- Research and gather information
- Write and edit the content
- Publish and promote the content
Career center
Learners who complete Diabetes Disease Detection with XG-Boost and Neural Networks will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops and deploys machine learning models to solve complex problems. This course will help you build the foundation needed to apply machine learning algorithms and build models that can predict the likelihood of diabetes.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops and deploys artificial intelligence systems to solve complex problems and automate tasks. This course will help you understand the use of neural networks and how they can be applied to medical diagnostic data to predict diabetes.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course may be useful in helping you understand techniques used to analyze medical diagnostic data to predict the likelihood of diabetes.
Statistician
Statistician
A Statistician collects, analyzes and interprets data to provide insights and make informed decisions. This course may be useful in helping you understand statistical techniques used to analyze medical diagnostic data to predict the likelihood of diabetes.
Software Engineer
Software Engineer
A Software Engineer designs, develops and maintains software applications and systems. This course may be useful in helping you develop the technical skills needed to build and deploy machine learning models that can predict the likelihood of diabetes.
Data Analyst
Data Analyst
A Data Analyst collects, processes and analyzes data to identify trends, patterns and insights. This course may be useful in helping you understand techniques used to analyze medical diagnostic data to predict the likelihood of diabetes.
Medical Researcher
Medical Researcher
A Medical Researcher conducts research to develop new treatments and cures for diseases. This course may be useful in helping you understand how to use machine learning techniques to analyze medical diagnostic data and develop models that can predict the likelihood of diabetes.
Public Health Educator
Public Health Educator
A Public Health Educator develops and implements educational programs to promote health and prevent disease. This course may be useful in helping you understand how to use data analysis techniques to identify trends and patterns in medical diagnostic data, which can be used to develop targeted educational programs to prevent diabetes.
Health Informatics Specialist
Health Informatics Specialist
A Health Informatics Specialist uses technology to improve the quality and efficiency of healthcare. This course may be useful in helping you learn about the role of data analysis in improving the diagnosis and treatment of diabetes.
Diabetes Educator
Diabetes Educator
A Diabetes Educator provides education and support to people with diabetes to help them manage their condition. This course may be useful in helping you understand the role of data analysis in developing personalized treatment plans and providing targeted education to people with diabetes.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and systems to identify opportunities for improvement. This course may be useful in helping you understand how to use data analysis techniques to identify trends and patterns in medical diagnostic data, which can be used to predict the likelihood of diabetes.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve complex problems in business and industry. This course may be useful in helping you develop the skills needed to analyze medical diagnostic data and develop models to predict the likelihood of diabetes.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and distribution of diseases in populations. This course may be useful in helping you understand how to use data analysis techniques to identify trends and patterns in medical diagnostic data, which can be used to predict the likelihood of diabetes.
Healthcare Data Analyst
Healthcare Data Analyst
A Healthcare Data Analyst analyzes healthcare data to identify trends, patterns, and opportunities for improvement. This course may be useful in helping you learn about the role of data analysis in improving the diagnosis, treatment, and prevention of diabetes.
Health Policy Analyst
Health Policy Analyst
A Health Policy Analyst analyzes health policies and programs to assess their effectiveness and impact. This course may be useful in helping you understand how to use data analysis techniques to evaluate the effectiveness of diabetes prevention and treatment programs.
For more career information including salaries, visit:
OpenCourser.com/course/0cnze7/diabetes
Reading list
We've selected ten 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
Diabetes Disease Detection with XG-Boost and Neural Networks.
Provides a comprehensive overview of machine learning with R. It covers all the major topics, including supervised and unsupervised learning, as well as deep learning. It valuable resource for anyone who wants to learn more about machine learning with R.
This comprehensive guide to machine learning with Python. It covers all the major techniques, including supervised and unsupervised learning, as well as deep learning. It valuable resource for anyone who wants to learn more about machine learning.
This free online book that covers the basics of machine learning. It valuable resource for anyone who wants to learn more about machine learning.
Offers a practical guide to deep learning concepts and techniques. It is valuable for learners seeking to implement neural networks for diabetes detection and gain insights into their inner workings.
Provides a comprehensive introduction to machine learning with a focus on Python libraries. It valuable reference for learners who want to gain practical experience in implementing XG-boost and neural networks.
This gentle introduction to machine learning. It valuable resource for anyone who wants to learn more about machine learning without getting bogged down in the technical details.
Save
This comprehensive textbook on deep learning. It covers all the major topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone who wants to learn more about deep learning.
Offers a beginner-friendly introduction to machine learning concepts. It useful resource for learners who want to gain a basic understanding of machine learning before diving into more advanced topics.
Provides comprehensive information on diabetes management, including nutrition, exercise, and stress management. It valuable resource for learners who want to gain a holistic understanding of diabetes care.
Provides a rigorous mathematical introduction to machine learning. It valuable resource for learners who want to gain a deep understanding of the theoretical foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0cnze7/diabetes
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Develops skills that are highly relevant to industry
Strong fit for learners who are entering or early in this field
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