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Sonya Makhni
and
Paul Cerrato
Examines data mining perspectives and methods in a healthcare context. Introduces the theoretical foundations for major data mining methods and studies how to select and use the appropriate data mining method and the major advantages for each. Students are exposed to contemporary data mining software applications and basic programming skills. Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling.
Register for this course and see more details by visiting:
OpenCourser.com/course/03xyl3/machine
What's inside
Syllabus
Demystifying Data Mining and Artificial Intelligence
In this module, we’ll start demystifying the terminology. We’ll begin by exploring the differences between AI, machine learning and deep learning. You’ll also gain hands-on experience in planning your own AI algorithm development, and learn what goes into preparing and constructing datasets for research questions.
Read more
Syllabus
Demystifying Data Mining and Artificial Intelligence
In this module, we’ll start demystifying the terminology. We’ll begin by exploring the differences between AI, machine learning and deep learning. You’ll also gain hands-on experience in planning your own AI algorithm development, and learn what goes into preparing and constructing datasets for research questions.
Read more
Exploring the AI/Machine Learning Toolbox
In this module, we’ll take a deep dive into several sophisticated AI modeling techniques, including random forest modeling, gradient boosting, clustering and neural networks.
Practical Application of AI/Machine Learning
In this module, you’ll dive deeper into the nitty gritty of how AI algorithms are trained and validated, and examine how they compare to clinicians in the field.
The Credibility Gap
In this module, we’ll explore why so many potentially useful algorithms are not being implemented by healthcare providers. That critique will explore the black box dilemma, and the challenges involved in developing accurate and equitable data sets. That means examining the many ways in which algorithms can discriminate against various marginalized segments of the population.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for the curious non-technical learner with some basic data mining knowledge who would like to learn more about the application of data mining techniques in a healthcare setting
Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling, which are needed skills in the field
Contemporary data mining software applications and basic programming skills are covered
Taught by instructors who are experts in data mining
Provides a theoretical foundation for major data mining methods
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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 Machine Learning in Healthcare: Fundamentals & Applications with these
activities:
Review the basics of data mining
Show steps
Go over fundamental terms and methods used in data mining
Browse courses on
Data Mining
Show steps
-
Read the first chapter of the course textbook or online materials
-
Watch online videos or tutorials on data mining
-
Take a practice quiz on data mining
Seek guidance from a mentor in the field
Show steps
Connect with someone who can provide insights and support
Show steps
-
Identify potential mentors through professional networks or online platforms
-
Reach out to mentors and request their guidance
-
Meet with mentors regularly for advice and feedback
Review programming basics
Show steps
Brushing up on programming basics will strengthen your foundation for understanding data mining concepts and techniques covered in this course.
Browse courses on
Data Mining
Show steps
-
Review basic programming concepts, such as data types, variables, and loops.
-
Practice writing simple programs in a programming language of your choice, such as Python or R.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Join a study group with other students
Show steps
Collaborate with peers to clarify concepts and deepen understanding
Show steps
-
Find a study group or start one with classmates
-
Meet regularly to discuss course materials
-
Work together on assignments and projects
Follow a TensorFlow tutorial on image classification in healthcare
Show steps
TensorFlow is an excellent deep learning library that is widely used in healthcare. Following a tutorial will give you an edge using it and the chance to explore some unique applications of machine learning in healthcare.
Browse courses on
TensorFlow
Show steps
-
Find a tutorial on image classification in healthcare using TensorFlow.
-
Set up your development environment and install TensorFlow.
-
Follow the tutorial and complete the image classification task.
Explore the scikit-learn GitHub repository
Show steps
Scikit-learn is a huge repository of machine learning algorithms that are common in healthcare. Understanding how to use it will be a valuable tool.
Browse courses on
scikit-learn
Show steps
-
Browse the documentation on scikit-learn's GitHub repository.
-
Choose an algorithm or module that is used in healthcare and explore the code.
-
If you are comfortable with Python, try your hand at contributing to the scikit-learn library.
Explore data mining techniques with guided tutorials
Show steps
Following guided tutorials will provide you with hands-on experience in applying data mining techniques to real-world datasets.
Browse courses on
Clustering
Show steps
-
Identify a data mining technique you want to learn more about, such as clustering or classification.
-
Find a guided tutorial that covers the technique in depth.
-
Follow the tutorial step-by-step and complete the exercises.
Read 'Data Mining: Practical Machine Learning Tools and Techniques'
Show steps
Deepen understanding of data mining through a comprehensive textbook
View
Data Mining: Practical Machine Learning Tools...
on Amazon
Show steps
-
Read one chapter each week
-
Take notes on key concepts
-
Complete the exercises at the end of each chapter
Solve data mining practice problems
Show steps
Solving practice problems will reinforce your understanding of data mining concepts and algorithms.
Show steps
-
Find a collection of data mining practice problems online or in textbooks.
-
Choose a problem that you are comfortable with and try to solve it on your own.
-
If you get stuck, refer to the solution or ask for help from a classmate or instructor.
Create a dataset of healthcare AI examples
Show steps
Collecting examples of AI applications in healthcare will give you a wide range of resources.
Show steps
-
Search reputable online sources for the latest examples of AI applications.
-
Include information such as the name of the application, the organization that developed it, and a brief description of its functionality.
-
Organize the examples into categories such as diagnosis, treatment, or research.
Contribute to an open-source data mining project
Show steps
Gain hands-on experience with data mining in a real-world environment
Show steps
-
Find an open-source data mining project on platforms like GitHub
-
Identify an issue or feature you can contribute to
-
Submit a pull request with your contribution
Build a data mining model for a real-world problem
Show steps
Applying data mining techniques to a real-world problem will deepen your understanding of the process and its practical applications in healthcare.
Show steps
-
Identify a real-world problem that can be addressed using data mining techniques.
-
Collect and prepare the necessary data.
-
Choose appropriate data mining algorithms and apply them to the data.
-
Evaluate the results and draw conclusions.
Participate in data mining competitions
Show steps
Push the boundaries of your skills and gain recognition
Show steps
-
Find data mining competitions online
-
Choose a competition to participate in
-
Build a data mining model to compete in the competition
Develop a data mining project
Show steps
Apply data mining techniques to solve a real-world problem
Show steps
-
Identify a problem that can be solved using data mining
-
Collect and prepare the data
-
Build a data mining model
-
Evaluate the model
-
Write a report on the project
Review the basics of data mining
Show steps
Go over fundamental terms and methods used in data mining
Browse courses on
Data Mining
Show steps
Show steps
Show steps
- Read the first chapter of the course textbook or online materials
- Watch online videos or tutorials on data mining
- Take a practice quiz on data mining
Seek guidance from a mentor in the field
Show steps
Connect with someone who can provide insights and support
Show steps
Show steps
Show steps
- Identify potential mentors through professional networks or online platforms
- Reach out to mentors and request their guidance
- Meet with mentors regularly for advice and feedback
Review programming basics
Show steps
Brushing up on programming basics will strengthen your foundation for understanding data mining concepts and techniques covered in this course.
Browse courses on
Data Mining
Show steps
Show steps
Show steps
- Review basic programming concepts, such as data types, variables, and loops.
- Practice writing simple programs in a programming language of your choice, such as Python or R.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Join a study group with other students
Show steps
Collaborate with peers to clarify concepts and deepen understanding
Show steps
Show steps
Show steps
- Find a study group or start one with classmates
- Meet regularly to discuss course materials
- Work together on assignments and projects
Follow a TensorFlow tutorial on image classification in healthcare
Show steps
TensorFlow is an excellent deep learning library that is widely used in healthcare. Following a tutorial will give you an edge using it and the chance to explore some unique applications of machine learning in healthcare.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Find a tutorial on image classification in healthcare using TensorFlow.
- Set up your development environment and install TensorFlow.
- Follow the tutorial and complete the image classification task.
Explore the scikit-learn GitHub repository
Show steps
Scikit-learn is a huge repository of machine learning algorithms that are common in healthcare. Understanding how to use it will be a valuable tool.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Browse the documentation on scikit-learn's GitHub repository.
- Choose an algorithm or module that is used in healthcare and explore the code.
- If you are comfortable with Python, try your hand at contributing to the scikit-learn library.
Explore data mining techniques with guided tutorials
Show steps
Following guided tutorials will provide you with hands-on experience in applying data mining techniques to real-world datasets.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Identify a data mining technique you want to learn more about, such as clustering or classification.
- Find a guided tutorial that covers the technique in depth.
- Follow the tutorial step-by-step and complete the exercises.
Read 'Data Mining: Practical Machine Learning Tools and Techniques'
Show steps
Deepen understanding of data mining through a comprehensive textbook
View
Data Mining: Practical Machine Learning Tools...
on Amazon
Show steps
Show steps
Show steps
- Read one chapter each week
- Take notes on key concepts
- Complete the exercises at the end of each chapter
Solve data mining practice problems
Show steps
Solving practice problems will reinforce your understanding of data mining concepts and algorithms.
Show steps
Show steps
Show steps
- Find a collection of data mining practice problems online or in textbooks.
- Choose a problem that you are comfortable with and try to solve it on your own.
- If you get stuck, refer to the solution or ask for help from a classmate or instructor.
Create a dataset of healthcare AI examples
Show steps
Collecting examples of AI applications in healthcare will give you a wide range of resources.
Show steps
Show steps
Show steps
- Search reputable online sources for the latest examples of AI applications.
- Include information such as the name of the application, the organization that developed it, and a brief description of its functionality.
- Organize the examples into categories such as diagnosis, treatment, or research.
Contribute to an open-source data mining project
Show steps
Gain hands-on experience with data mining in a real-world environment
Show steps
Show steps
Show steps
- Find an open-source data mining project on platforms like GitHub
- Identify an issue or feature you can contribute to
- Submit a pull request with your contribution
Build a data mining model for a real-world problem
Show steps
Applying data mining techniques to a real-world problem will deepen your understanding of the process and its practical applications in healthcare.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using data mining techniques.
- Collect and prepare the necessary data.
- Choose appropriate data mining algorithms and apply them to the data.
- Evaluate the results and draw conclusions.
Participate in data mining competitions
Show steps
Push the boundaries of your skills and gain recognition
Show steps
Show steps
Show steps
- Find data mining competitions online
- Choose a competition to participate in
- Build a data mining model to compete in the competition
Develop a data mining project
Show steps
Apply data mining techniques to solve a real-world problem
Show steps
Show steps
Show steps
- Identify a problem that can be solved using data mining
- Collect and prepare the data
- Build a data mining model
- Evaluate the model
- Write a report on the project
Career center
Learners who complete Machine Learning in Healthcare: Fundamentals & Applications will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist applies statistical and analytical methods to get data insights. These insights can be used to create new products, improve decision making, and more. This course provides a strong foundation for Data Scientists, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills are essential for Data Scientists, as they need to be able to work with large and complex datasets. The course emphasizes solving real-world problems, which is another essential skill for Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds, deploys, and maintains machine learning models. This course provides a strong foundation for Machine Learning Engineers, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills are essential for Machine Learning Engineers, as they need to be able to work with large and complex datasets. The course emphasizes solving real-world problems, which is another essential skill for Machine Learning Engineers.
Health Informatics Specialist
Health Informatics Specialist
A Health Informatics Specialist uses data to improve the delivery of healthcare. This course provides a strong foundation for Health Informatics Specialists, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills are essential for Health Informatics Specialists, as they need to be able to work with large and complex datasets. The course emphasizes solving real-world problems, which is another essential skill for Health Informatics Specialists.
Healthcare Data Analyst
Healthcare Data Analyst
A Healthcare Data Analyst collects, analyzes, and interprets healthcare data. This data can be used to improve patient care, develop new products, and more. This course provides a strong foundation for Healthcare Data Analysts, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills are essential for Healthcare Data Analysts, as they need to be able to work with large and complex datasets. The course emphasizes solving real-world problems, which is another essential skill for Healthcare Data Analysts.
Clinical Research Associate
Clinical Research Associate
A Clinical Research Associate manages clinical trials. This course provides a strong foundation for Clinical Research Associates, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills are essential for Clinical Research Associates, as they need to be able to work with large and complex datasets. The course emphasizes solving real-world problems, which is another essential skill for Clinical Research Associates.
Biostatistician
Biostatistician
A Biostatistician uses statistical methods to analyze biological data. This data can be used to develop new drugs, improve patient care, and more. This course provides a strong foundation for Biostatisticians, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills are essential for Biostatisticians, as they need to be able to work with large and complex datasets. The course emphasizes solving real-world problems, which is another essential skill for Biostatisticians.
Epidemiologist
Epidemiologist
An Epidemiologist studies the distribution and determinants of health-related states or events in specified populations. This course provides a strong foundation for Epidemiologists, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills are essential for Epidemiologists, as they need to be able to work with large and complex datasets. The course emphasizes solving real-world problems, which is another essential skill for Epidemiologists.
Public Health Analyst
Public Health Analyst
A Public Health Analyst collects, analyzes, and interprets data to improve public health. This course provides a strong foundation for Public Health Analysts, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills are essential for Public Health Analysts, as they need to be able to work with large and complex datasets. The course emphasizes solving real-world problems, which is another essential skill for Public Health Analysts.
Healthcare Consultant
Healthcare Consultant
A Healthcare Consultant provides advice to healthcare organizations on how to improve their operations. This course may be useful for Healthcare Consultants, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills may be helpful for Healthcare Consultants, as they need to be able to understand and use data to make recommendations to healthcare organizations.
Healthcare Administrator
Healthcare Administrator
A Healthcare Administrator plans, directs, and coordinates healthcare services. This course may be useful for Healthcare Administrators, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills may be helpful for Healthcare Administrators, as they need to be able to understand and use data to make informed decisions.
Health Policy Analyst
Health Policy Analyst
A Health Policy Analyst develops and evaluates policies that affect healthcare. This course may be useful for Health Policy Analysts, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills may be helpful for Health Policy Analysts, as they need to be able to understand and use data to make informed policy decisions.
Pharmaceutical Sales Representative
Pharmaceutical Sales Representative
A Pharmaceutical Sales Representative sells pharmaceutical products to healthcare providers. This course may be useful for Pharmaceutical Sales Representatives, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills may be helpful for Pharmaceutical Sales Representatives, as they need to be able to understand and use data to identify and target potential customers.
Medical Writer
Medical Writer
A Medical Writer creates written materials about medical topics. This course may be useful for Medical Writers, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills may be helpful for Medical Writers, as they need to be able to understand and use data to create accurate and informative medical content.
Healthcare IT Specialist
Healthcare IT Specialist
A Healthcare IT Specialist implements and manages healthcare information systems. This course may be useful for Healthcare IT Specialists, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills may be helpful for Healthcare IT Specialists, as they need to be able to understand and use data to improve the efficiency and effectiveness of healthcare information systems.
Healthcare Marketer
Healthcare Marketer
A Healthcare Marketer develops and implements marketing campaigns for healthcare products and services. This course may be useful for Healthcare Marketers, as it covers the major data mining methods, including random forest modeling, gradient boosting, clustering and neural networks. It also covers the basics of data cleaning, data transformation, and data modeling. These skills may be helpful for Healthcare Marketers, as they need to be able to understand and use data to create effective marketing campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/03xyl3/machine
Reading list
We've selected seven 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 in Healthcare: Fundamentals & Applications.
Save
Classic textbook on deep learning. It covers a wide range of topics, from the basics of deep learning to its applications in various fields.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for both beginners and experienced practitioners in the field.
Save
Classic textbook on data mining. It covers a wide range of topics, from the basics of data mining to its applications in various fields.
Provides a comprehensive overview of machine learning algorithms, covering topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a hands-on introduction to machine learning using Python, covering topics such as data preprocessing, model training, and model evaluation.
Provides a practical guide to data analysis techniques used in machine learning, such as data cleaning, feature engineering, and data visualization.
Save
Provides a comprehensive overview of data mining for healthcare, covering topics such as data preprocessing, feature selection, clustering, and classification. It valuable resource for anyone interested in learning more about the application of data mining to healthcare problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/03xyl3/machine
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Level
Introductory
Via
Coursera
Institution
Northeastern University
Instructors
Sonya Makhni
Paul Cerrato
Language
English
Good to know
Suitable for the curious non-technical learner with some basic data mining knowledge who would like to learn more about the application of data mining techniques in a healthcare setting
Focuses on solving real-world problems, which require data cleaning, data transformation, and data modeling, which are needed skills in the field
Contemporary data mining software applications and basic programming skills are covered
Taught by instructors who are experts in data mining
Provides a theoretical foundation for major data mining methods
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