ML: Diagnose the presence of Breast Cancer with Python

제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Learn how to set up a Jupyter notebook, load data and convert it to data frame.

Preview and visualize loaded data.

Train, test and evaluate a machine learning model.

Clock1 hour
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

Machine LearningPython ProgrammingJupyter NotebookData Visualization (DataViz)Supervised Learning

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. By the end of Task 1, you will get an overview of this guided project, Jupyter notebooks which will be used and how you will have set up your notebook environment for this project.

  2. By the end of Task 2, you will have begun the process of building the project template by first loading the data, previewing and exploring it.

  3. By the end of Task 3, you will have checked for missing values, explored data types and visualized features in the data using seaborn.

  4. By the end of Task 4, you will have trained different classifier models, run predictions with them and evaluate their various performances using accuracy score.

  5. By the end of Task 5, you will have combined your predictions with test features and saved your outputs in CSV file format.

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

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