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Ikechukwu Nigel Ogbuchi
In this 1-hour long project-based course, you will learn how to set up and run your Jupyter Notebook, load, preview and visualize data, then train, test and evaluate a machine learning model that predicts if a patient has breast cancer or not.
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/3q9jz8/ml
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides an efficient method to explore Jupyter Notebooks, load, preview, and visualize data
Utilizes a project-based approach, offering learners a hands-on experience in machine learning model training, testing, and evaluation
Primarily designed for learners located in the North America region, fostering a sense of community and region-specific relevance
Suitable for beginners seeking an introduction to machine learning concepts and their application in breast cancer prediction
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Reviews summary
Ml for breast cancer diagnostics
This concise, 1-hour project-based course on ML fundamentals is best-suited for learners in North America. Students will set up and run a Jupyter Notebook, load, preview, and visualize data, then train, test, and evaluate a machine learning model that helps diagnose breast cancer.
Straightforward, easy lessons.
"simple and straightforward"
Applicable, hands-on training.
"Es un proyecto muy practico y aplicable"
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 ML: Diagnose the presence of Breast Cancer with Python with these
activities:
Review Statistical Concepts for Machine Learning
Show steps
Strengthen your foundation by reviewing statistical concepts essential for understanding machine learning algorithms.
Browse courses on
Statistics
Show steps
-
Review key statistical concepts, such as probability distributions and hypothesis testing.
-
Solve practice problems to reinforce your understanding.
-
Identify resources for further learning and reference.
Follow Coursera's Guided Project Tutorials
Show steps
Supplement your learning by following step-by-step tutorials that cover essential aspects of the course.
Browse courses on
Jupyter Notebook
Show steps
-
Identify relevant tutorials from Coursera's online library.
-
Follow the instructions and complete the hands-on exercises.
-
Review the provided materials and explore additional resources.
Organize and Review Course Materials
Show steps
Enhance your retention by compiling, reviewing, and synthesizing course materials.
Show steps
-
Organize notes, assignments, and lecture slides.
-
Review materials regularly to reinforce your understanding.
-
Identify areas where you need further clarification or practice.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Practice Problems on LeetCode
Show steps
Reinforce your understanding by solving coding challenges and practicing algorithm implementation.
Browse courses on
Machine Learning Algorithms
Show steps
-
Select problems that align with the concepts covered in the course.
-
Attempt to solve the problems independently.
-
Review solutions and identify areas for improvement.
-
Repeat the process to refine your problem-solving skills.
Build a Machine Learning Model for Breast Cancer Detection
Show steps
Apply your knowledge by creating a practical project that demonstrates your ability to build a machine learning model.
Show steps
-
Gather and prepare a dataset for breast cancer detection.
-
Select and train a machine learning model using appropriate algorithms.
-
Evaluate the performance of your model and fine-tune parameters.
-
Document your project and share your findings.
Participate in Machine Learning Hackathons
Show steps
Challenge yourself and test your skills by participating in hackathons that focus on machine learning applications.
Show steps
-
Identify relevant hackathons and register to participate.
-
Form a team or collaborate with others to tackle the challenge.
-
Develop and implement a machine learning solution.
-
Present your solution and receive feedback.
Review Statistical Concepts for Machine Learning
Show steps
Strengthen your foundation by reviewing statistical concepts essential for understanding machine learning algorithms.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review key statistical concepts, such as probability distributions and hypothesis testing.
- Solve practice problems to reinforce your understanding.
- Identify resources for further learning and reference.
Follow Coursera's Guided Project Tutorials
Show steps
Supplement your learning by following step-by-step tutorials that cover essential aspects of the course.
Browse courses on
Jupyter Notebook
Show steps
Show steps
Show steps
- Identify relevant tutorials from Coursera's online library.
- Follow the instructions and complete the hands-on exercises.
- Review the provided materials and explore additional resources.
Organize and Review Course Materials
Show steps
Enhance your retention by compiling, reviewing, and synthesizing course materials.
Show steps
Show steps
Show steps
- Organize notes, assignments, and lecture slides.
- Review materials regularly to reinforce your understanding.
- Identify areas where you need further clarification or practice.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Practice Problems on LeetCode
Show steps
Reinforce your understanding by solving coding challenges and practicing algorithm implementation.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Select problems that align with the concepts covered in the course.
- Attempt to solve the problems independently.
- Review solutions and identify areas for improvement.
- Repeat the process to refine your problem-solving skills.
Build a Machine Learning Model for Breast Cancer Detection
Show steps
Apply your knowledge by creating a practical project that demonstrates your ability to build a machine learning model.
Show steps
Show steps
Show steps
- Gather and prepare a dataset for breast cancer detection.
- Select and train a machine learning model using appropriate algorithms.
- Evaluate the performance of your model and fine-tune parameters.
- Document your project and share your findings.
Participate in Machine Learning Hackathons
Show steps
Challenge yourself and test your skills by participating in hackathons that focus on machine learning applications.
Show steps
Show steps
Show steps
- Identify relevant hackathons and register to participate.
- Form a team or collaborate with others to tackle the challenge.
- Develop and implement a machine learning solution.
- Present your solution and receive feedback.
Career center
Learners who complete ML: Diagnose the presence of Breast Cancer with Python will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning systems. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Machine Learning Engineers with hands-on experience in building and evaluating machine learning models for medical diagnosis tasks. This course not only strengthens their technical skills but also demonstrates their ability to apply machine learning to real-world healthcare problems.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts collect, analyze, and interpret healthcare data to improve patient care and outcomes. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Healthcare Data Analysts with a strong foundation in machine learning techniques and algorithms. The ability to develop and deploy machine learning models to diagnose breast cancer can be a valuable skill for Healthcare Data Analysts working in cancer research or clinical settings.
Data Scientist
Data Scientist
Data Scientists collect and analyze vast amounts of structured and unstructured data to extract meaningful insights. Courses like "ML: Diagnose the presence of Breast Cancer with Python" provide Data Scientists with a solid foundation in machine learning techniques and algorithms. The ability to build and deploy machine learning models to diagnose breast cancer can be a valuable skill for Data Scientists working in the healthcare industry.
Medical Physicist
Medical Physicist
Medical Physicists use their knowledge of physics and technology to develop and apply medical imaging techniques. Many Medical Physicists specialize in cancer diagnosis and treatment planning. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Medical Physicists with a deeper understanding of machine learning algorithms and their potential applications in breast cancer diagnosis. This course can help Medical Physicists stay up-to-date with the latest advancements in machine learning and improve their ability to develop and deploy machine learning-based diagnostic tools.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. Many Biostatisticians work on cancer research and clinical trials. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Biostatisticians with a deeper understanding of machine learning algorithms and their potential applications in cancer diagnosis and clinical research. This course can help Biostatisticians stay up-to-date with the latest advancements in machine learning and improve their ability to design and analyze clinical studies.
Cancer Researcher
Cancer Researcher
Cancer Researchers study the causes, diagnosis, and treatment of cancer. Many Cancer Researchers specialize in breast cancer research. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Cancer Researchers with a deeper understanding of machine learning algorithms and their potential applications in breast cancer diagnosis and research. This course can help Cancer Researchers stay up-to-date with the latest advancements in machine learning and improve their ability to design and conduct clinical studies.
Medical Imaging Technician
Medical Imaging Technician
Medical Imaging Technicians operate and maintain medical imaging equipment, such as MRI and CT scanners. Many Medical Imaging Technicians specialize in breast imaging. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Medical Imaging Technicians with a deeper understanding of machine learning algorithms and their potential applications in breast cancer diagnosis. This course can help Medical Imaging Technicians stay up-to-date with the latest advancements in machine learning and improve their ability to operate and maintain medical imaging equipment.
Nurse Practitioner
Nurse Practitioner
Nurse Practitioners provide primary care to patients. Many Nurse Practitioners specialize in oncology or breast cancer care. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Nurse Practitioners with a deeper understanding of machine learning algorithms and their potential applications in breast cancer diagnosis and care. This course can help Nurse Practitioners stay up-to-date with the latest advancements in machine learning and improve their ability to provide personalized and effective care for breast cancer patients.
Radiation Therapist
Radiation Therapist
Radiation Therapists administer radiation therapy to patients with cancer. Many Radiation Therapists specialize in breast cancer treatment. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Radiation Therapists with a deeper understanding of machine learning algorithms and their potential applications in breast cancer diagnosis and treatment planning. This course can help Radiation Therapists stay up-to-date with the latest advancements in machine learning and improve their ability to deliver safe and effective radiation therapy treatments.
Health Informatics Specialist
Health Informatics Specialist
Health Informatics Specialists use technology to improve the efficiency and effectiveness of healthcare delivery. Many Health Informatics Specialists work in cancer research or clinical settings. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Health Informatics Specialists with a deeper understanding of machine learning algorithms and their potential applications in breast cancer diagnosis and treatment planning. This course can help Health Informatics Specialists stay up-to-date with the latest advancements in machine learning and improve their ability to develop and implement innovative healthcare solutions.
Breast Surgeon
Breast Surgeon
Breast Surgeons perform surgery to treat breast cancer. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Breast Surgeons with a deeper understanding of machine learning algorithms and their potential applications in breast cancer diagnosis and treatment planning. This course can help Breast Surgeons stay up-to-date with the latest advancements in machine learning and improve their ability to provide personalized and effective surgical care for breast cancer patients.
Oncologist
Oncologist
Oncologists diagnose and treat cancer. Many Oncologists specialize in breast cancer. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Oncologists with a deeper understanding of machine learning algorithms and their potential applications in breast cancer diagnosis and treatment planning. This course can help Oncologists stay up-to-date with the latest advancements in machine learning and improve their ability to provide personalized and effective cancer care.
Healthcare Administrator
Healthcare Administrator
Healthcare Administrators manage the operations of healthcare organizations. Many Healthcare Administrators work in cancer research or clinical settings. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Healthcare Administrators with a deeper understanding of machine learning algorithms and their potential applications in breast cancer diagnosis and treatment planning. This course can help Healthcare Administrators stay up-to-date with the latest advancements in machine learning and improve their ability to make informed decisions about the adoption and implementation of new technologies.
Public Health Specialist
Public Health Specialist
Public Health Specialists work to improve the health of communities. Many Public Health Specialists work in cancer prevention and control. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Public Health Specialists with a deeper understanding of machine learning algorithms and their potential applications in breast cancer screening and prevention programs. This course can help Public Health Specialists stay up-to-date with the latest advancements in machine learning and improve their ability to develop and implement effective public health interventions.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states or events in specified populations. Many Epidemiologists work in cancer research and prevention. The "ML: Diagnose the presence of Breast Cancer with Python" course provides Epidemiologists with a deeper understanding of machine learning algorithms and their potential applications in breast cancer surveillance and research. This course can help Epidemiologists stay up-to-date with the latest advancements in machine learning and improve their ability to design and conduct epidemiological studies.
For more career information including salaries, visit:
OpenCourser.com/course/3q9jz8/ml
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
ML: Diagnose the presence of Breast Cancer with Python.
Provides a comprehensive overview of deep learning with Python, covering the basics of neural networks, convolutional neural networks, and recurrent neural networks. It is written in a clear and concise style, with plenty of examples and exercises to help readers learn the material.
Provides a comprehensive overview of natural language processing with Python, covering the basics of text preprocessing, natural language understanding, and natural language generation. It is written in a clear and concise style, with plenty of examples and exercises to help readers learn the material.
Save
Provides a comprehensive overview of machine learning with Python, covering the basics of supervised and unsupervised learning, as well as more advanced topics such as deep learning and natural language processing. It is written in a clear and concise style, with plenty of examples and exercises to help readers learn the material.
Provides a comprehensive overview of machine learning with R, covering the basics of supervised and unsupervised learning, as well as more advanced topics such as deep learning and natural language processing. It is written in a clear and concise style, with plenty of examples and exercises to help readers learn the material.
Provides a more hands-on approach to machine learning. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Provides a practical introduction to machine learning with Java, using the popular Weka library. It covers a wide range of topics, from data preprocessing to model evaluation, and includes plenty of hands-on exercises to help readers learn the material.
Save
Provides a mathematical foundation for machine learning. It covers topics such as linear algebra, calculus, and probability.
Provides a patient-friendly guide to breast cancer. It covers topics such as diagnosis, treatment, and support.
Provides a gentle introduction to machine learning using Python. It covers topics such as data preprocessing, feature engineering, and model evaluation.
Provides a patient-friendly guide to breast cancer. It covers topics such as diagnosis, treatment, and support.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3q9jz8/ml
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ikechukwu Nigel Ogbuchi
Good to know
Provides an efficient method to explore Jupyter Notebooks, load, preview, and visualize data
Utilizes a project-based approach, offering learners a hands-on experience in machine learning model training, testing, and evaluation
Primarily designed for learners located in the North America region, fostering a sense of community and region-specific relevance
Suitable for beginners seeking an introduction to machine learning concepts and their application in breast cancer prediction
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