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Daniel Romaniuk
In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this course, you will be able to model an existing dataset with the goal of making predictions about new data. This is a first step on the path to mastering machine learning.
Read more
In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this course, you will be able to model an existing dataset with the goal of making predictions about new data. This is a first step on the path to mastering machine learning.
Read more
In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this course, you will be able to model an existing dataset with the goal of making predictions about new data. This is a first step on the path to mastering machine learning.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/91lb8o/medical
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What's inside
Syllabus
Project Overview
In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this project, you will have created a machine learning model using industry standard tools, and solved a real world medical diagnosis problem.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops a model using industry standard Python tools
Explores medical diagnosis using real-world data
Appropriate for beginners with basic computer literacy
Involves hands-on project
Taught by instructors with experience in data science
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Save Medical Diagnosis using Support Vector Machines to your list so you can find it easily later:
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Reviews summary
Medical svm diagnosis summary
Learners say this course offers effective and useful examples of SVM models. It is quality introductory course for training SVM classifiers for those who are relatively new to the topic. However, those looking for more in-depth information regarding SVM may be disappointed -- this is a basic course and also excludes data engineering, such as building the dataset used by the model from raw data.
Appropriate for those new to the topic
"Quick and basic intro to SVM training."
"Clearly explained each step and pointed out some issues to avoid."
Effective examples
"Just the right amount of explanation and content."
"A very simple example"
"This will give you some insights regarding the power of SVMs"
Beginner-level knowledge
"I'd have liked a little explanation of *how* SVMs work."
Lacks data engineering content
"Showed good use of the SVM classifier on real medical diabetes data."
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 Medical Diagnosis using Support Vector Machines with these
activities:
Refresher on Linear Algebra
Show steps
Allows students to practice and review required linear algebra skills necessary for success in this course
Browse courses on
Linear Algebra
Show steps
-
Review the basics of vector spaces, matrices, and linear transformations
-
Practice solving systems of linear equations
-
Review the concepts of eigenvalues and eigenvectors
Course Materials Compilation
Show steps
Helps students organize and review course materials for better retention
Show steps
-
Gather all lecture notes, slides, and assignments
-
Organize the materials into a logical structure
-
Review the materials regularly
Peer-led Study Group on SVM
Show steps
Fosters collaboration and knowledge exchange among students
Browse courses on
SVM
Show steps
-
Form a study group with peers
-
Meet regularly to discuss SVM concepts
-
Work together on SVM-related projects
Four other activities
Expand to see all activities and additional details
Show all seven activities
Coursera's Guided Tutorial on SVM with Python
Show steps
Provides additional support and guidance on SVM concepts and implementation
Browse courses on
SVM
Show steps
-
Follow the Coursera tutorial on SVM with Python
-
Complete the practice exercises provided in the tutorial
-
Apply the learned concepts to a small project
Practice SVM Classification Exercises
Show steps
Develops the skills necessary to apply SVM for classification tasks
Browse courses on
SVM
Show steps
-
Solve practice problems on binary classification using SVM
-
Implement SVM algorithms from scratch
-
Apply SVM to real-world datasets
SVM-based Diabetes Diagnosis Project
Show steps
Provides hands-on experience in applying SVM to a real-world medical diagnosis problem
Browse courses on
SVM
Show steps
-
Gather and preprocess the diabetes dataset
-
Train and evaluate an SVM model for diabetes diagnosis
-
Interpret and analyze the results
Blog Post: SVM Applications in the Medical Field
Show steps
Encourages students to explore and demonstrate their understanding of SVM applications in medical diagnosis
Browse courses on
SVM
Show steps
-
Research SVM applications in the medical field
-
Identify a specific medical diagnosis problem
-
Develop an SVM model to address the problem
Refresher on Linear Algebra
Show steps
Allows students to practice and review required linear algebra skills necessary for success in this course
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the basics of vector spaces, matrices, and linear transformations
- Practice solving systems of linear equations
- Review the concepts of eigenvalues and eigenvectors
Course Materials Compilation
Show steps
Helps students organize and review course materials for better retention
Show steps
Show steps
Show steps
- Gather all lecture notes, slides, and assignments
- Organize the materials into a logical structure
- Review the materials regularly
Peer-led Study Group on SVM
Show steps
Fosters collaboration and knowledge exchange among students
Browse courses on
SVM
Show steps
Show steps
Show steps
- Form a study group with peers
- Meet regularly to discuss SVM concepts
- Work together on SVM-related projects
Four other activities
Expand to see all activities and additional details
Show all seven activities
Coursera's Guided Tutorial on SVM with Python
Show steps
Provides additional support and guidance on SVM concepts and implementation
Browse courses on
SVM
Show steps
Show steps
Show steps
- Follow the Coursera tutorial on SVM with Python
- Complete the practice exercises provided in the tutorial
- Apply the learned concepts to a small project
Practice SVM Classification Exercises
Show steps
Develops the skills necessary to apply SVM for classification tasks
Browse courses on
SVM
Show steps
Show steps
Show steps
- Solve practice problems on binary classification using SVM
- Implement SVM algorithms from scratch
- Apply SVM to real-world datasets
SVM-based Diabetes Diagnosis Project
Show steps
Provides hands-on experience in applying SVM to a real-world medical diagnosis problem
Browse courses on
SVM
Show steps
Show steps
Show steps
- Gather and preprocess the diabetes dataset
- Train and evaluate an SVM model for diabetes diagnosis
- Interpret and analyze the results
Blog Post: SVM Applications in the Medical Field
Show steps
Encourages students to explore and demonstrate their understanding of SVM applications in medical diagnosis
Browse courses on
SVM
Show steps
Show steps
Show steps
- Research SVM applications in the medical field
- Identify a specific medical diagnosis problem
- Develop an SVM model to address the problem
Career center
Learners who complete Medical Diagnosis using Support Vector Machines will develop knowledge and skills
that may be useful to these careers:
Physician Assistant
Physician Assistant
Physician Assistants are medical professionals who work under the supervision of a physician. They perform a variety of tasks, including taking patient histories, performing physical exams, ordering and interpreting tests, and prescribing medications. The course 'Medical Diagnosis using Support Vector Machines' can help Physician Assistants to improve their diagnostic skills by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Physician Assistants to make more accurate diagnoses and develop more effective treatment plans for their patients.
Medical Technologist
Medical Technologist
Medical Technologists are responsible for performing laboratory tests and analyzing the results. The course 'Medical Diagnosis using Support Vector Machines' can help Medical Technologists to improve their diagnostic skills by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Medical Technologists to make more accurate diagnoses and develop more effective treatment plans for their patients.
Clinical Laboratory Scientist
Clinical Laboratory Scientist
Clinical Laboratory Scientists are responsible for performing laboratory tests and analyzing the results. The course 'Medical Diagnosis using Support Vector Machines' can help Clinical Laboratory Scientists to improve their diagnostic skills by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Clinical Laboratory Scientists to make more accurate diagnoses and develop more effective treatment plans for their patients.
Nurse Practitioner
Nurse Practitioner
Nurse Practitioners are advanced practice nurses who have the ability to diagnose and treat illnesses, prescribe medications, and provide preventative care. The course 'Medical Diagnosis using Support Vector Machines' can help Nurse Practitioners to improve their diagnostic skills by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Nurse Practitioners to make more accurate diagnoses and develop more effective treatment plans for their patients.
Epidemiologist
Epidemiologist
Epidemiologists are responsible for investigating the causes of disease and developing strategies to prevent and control it. The course 'Medical Diagnosis using Support Vector Machines' can help Epidemiologists to improve their skills in investigating the causes of disease by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Epidemiologists to identify risk factors for disease and to develop more effective strategies to prevent and control it.
Biostatistician
Biostatistician
Biostatisticians are responsible for designing and analyzing studies to assess the effectiveness of medical treatments. The course 'Medical Diagnosis using Support Vector Machines' can help Biostatisticians to improve their skills in designing and analyzing studies by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Biostatisticians to design more effective studies and to make more accurate predictions about the effectiveness of medical treatments.
Health Policy Analyst
Health Policy Analyst
Health Policy Analysts are responsible for developing and evaluating health policy. The course 'Medical Diagnosis using Support Vector Machines' can help Health Policy Analysts to improve their skills in developing and evaluating health policy by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Health Policy Analysts to make more informed decisions about health policy and to develop more effective policies that improve the health of the population.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. The course 'Medical Diagnosis using Support Vector Machines' can help Data Analysts to improve their skills in collecting, cleaning, and analyzing data by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Data Analysts to make more informed decisions about data and to develop more effective data-driven solutions.
Medical Writer
Medical Writer
Medical Writers are responsible for writing clear and accurate medical information for a variety of audiences. The course 'Medical Diagnosis using Support Vector Machines' can help Medical Writers to improve their skills in writing clear and accurate medical information by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Medical Writers to write more informative and engaging medical content.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. The course 'Medical Diagnosis using Support Vector Machines' can help Machine Learning Engineers to improve their skills in designing, building, and deploying machine learning models by providing them with a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Machine Learning Engineers to build more effective machine learning models and to deploy them in a variety of applications.
Computer Scientist
Computer Scientist
Computer Scientists are responsible for developing new computer technologies and applications. The course 'Medical Diagnosis using Support Vector Machines' may be useful to Computer Scientists who are interested in developing new computer technologies and applications for the healthcare industry. The course provides a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Computer Scientists to develop more effective computer technologies and applications for the healthcare industry.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. The course 'Medical Diagnosis using Support Vector Machines' may be useful to Statisticians who are interested in working in the healthcare industry. The course provides a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Statisticians to develop more effective statistical methods for the healthcare industry.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, building, and maintaining software systems. The course 'Medical Diagnosis using Support Vector Machines' may be useful to Software Engineers who are interested in developing software for the healthcare industry. The course provides a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Software Engineers to develop more effective software for the healthcare industry.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. The course 'Medical Diagnosis using Support Vector Machines' may be useful to Data Scientists who are interested in working in the healthcare industry. The course provides a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Data Scientists to develop more effective data-driven solutions for the healthcare industry.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers are responsible for designing and developing medical devices and technologies. The course 'Medical Diagnosis using Support Vector Machines' may be useful to Biomedical Engineers who are interested in developing medical devices and technologies for the diagnosis and treatment of disease. The course provides a foundational understanding of support vector machines, a powerful machine learning algorithm that can be used to identify patterns in data. This understanding can help Biomedical Engineers to develop more effective medical devices and technologies for the healthcare industry.
For more career information including salaries, visit:
OpenCourser.com/course/91lb8o/medical
Reading list
We've selected nine 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
Medical Diagnosis using Support Vector Machines.
Practical guide to machine learning using Python and the scikit-learn, Keras, and TensorFlow libraries. It valuable resource for anyone who wants to learn how to apply machine learning to real-world problems.
Comprehensive introduction to statistical learning, and it valuable resource for anyone who wants to learn more about the field. It covers a wide range of topics, including linear regression, logistic regression, support vector machines, and decision trees.
Comprehensive introduction to pattern recognition and machine learning, and it valuable resource for anyone who wants to learn more about the field. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Save
Provides a comprehensive overview of artificial intelligence in healthcare, and it valuable resource for anyone who wants to learn more about the field.
Provides a comprehensive overview of machine learning in medicine, and it valuable resource for anyone who wants to learn more about the field.
Save
Comprehensive introduction to statistical methods for medical research, and it valuable resource for anyone who wants to learn more about the field.
Practical guide to medical statistics from scratch, and it valuable resource for anyone who wants to learn how to apply medical statistics to healthcare problems.
Provides a comprehensive overview of clinical trials, and it valuable resource for anyone who wants to learn more about the field.
Provides a comprehensive overview of data management for clinical trials, and it valuable resource for anyone who wants to learn more about the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/91lb8o/medical
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Daniel Romaniuk
Language
English
Good to know
Develops a model using industry standard Python tools
Explores medical diagnosis using real-world data
Appropriate for beginners with basic computer literacy
Involves hands-on project
Taught by instructors with experience in data science
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