Support Vector Machines in Python, From Start to Finish

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Neste Projeto guiado gratuito, você irá:

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

Showcase this hands-on experience in an interview

Clock2 hours
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em 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.


Some Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices.

Habilidades que você desenvolverá

Data ScienceMachine LearningPython ProgrammingSupport Vector Machine (SVM)classification

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  1. Import the modules that will do all the work

  2. Import the data

  3. Missing Data Part 1: Identifying Missing Data

  4. Missing Data Part 2: Dealing With Missing Data

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

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

  7. Format the Data Part 3: Centering and Scaling

  8. Build A Preliminary Support Vector Machine

  9. Optimize Parameters with Cross Validation

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

Como funcionam os projetos guiados

Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download

Em um vídeo de tela dividida, seu instrutor te orientará passo a passo




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