ML: Diagnose the presence of Breast Cancer with Python

von
Coursera Project Network
In diesem angeleitetes Projekt werden Sie:

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
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

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.

Kompetenzen, die Sie erwerben werden

  • Machine Learning
  • Python Programming
  • Jupyter Notebook
  • Data Visualization (DataViz)
  • Supervised Learning

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  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.

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

Häufig gestellte Fragen

Häufig gestellte Fragen

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