Support Vector Machines with scikit-learn

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Neste projeto guiado, você irá:

Understand the theory behind support vector machines

Builld SVM models with scikit-learn to classify linear and non-linear data

Determine the strengths and limitations of SVMs

Develop an SVM-based facial recognition model

Clock2.5 hours
BeginnerBásico
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

In this project, you will learn the functioning and intuition behind a powerful class of supervised linear models known as support vector machines (SVMs). By the end of this project, you will be able to apply SVMs using scikit-learn and Python to your own classification tasks, including building a simple facial recognition model. 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 Python, Jupyter, and scikit-learn pre-installed. 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.

Habilidades que você desenvolverá

Data ScienceMachine LearningPython ProgrammingSupport Vector Machine (SVM)Data Analysis

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. Getting Started

  2. Beyond Linear Discriminative Classifiers

  3. Many Possible Separators

  4. Plotting the Margins

  5. Training an SVM Model

  6. Facial Recognition with SVMs

  7. Preprocessing the data set

  8. Hyperparameter Tuning with Grid-Search Cross Validation

  9. Visualize Test Images

  10. Evaluating the Support Vector Classifier

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

Instrutores

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