Support Vector Machines in Python, From Start to Finish

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In diesem Guided Project werden Sie:

Import data into, and manipulating a pandas dataframe

Format the data for a support vector machine, including One-Hot Encoding and missing data.

Optimize parameters for the radial basis function and classification

Build, evaluate, draw and interpret a support vector machine

Clock4 hours/week
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with programming in Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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

Data ScienceMachine LearningPython ProgrammingSupport Vector Machine (SVM)classification

Schritt für Schritt lernen

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

  1. Task 1: Import the modules that will do all the work

  2. Task 2: Import the data

  3. Task 3: Missing Data Part 1: Identifying Missing Data

  4. Task 4: Missing Data Part 2: Dealing With Missing Data

  5. Task 5: Format Data Part 1: Split the Data into Dependent and Independent Variables

  6. Task 6: Format the Data Part 2: One-Hot Encoding

  7. Task 7: Format the Data Part 3: Centering and Scaling

  8. Task 8: Build A Preliminary Support Vector Machine

  9. Task 9: Optimize Parameters with Cross Validation

  10. Task 10: Building, Evaluating, Drawing, and Interpreting the Final Support Vector Machine

How Guided Projects work

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Häufig gestellte Fragen

Häufig gestellte Fragen

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