Support Vector Machines with scikit-learn

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Rhyme
Dans ce Guided Project, vous :

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
BeginnerDébutant
CloudAucun téléchargement requis
VideoVidéo en écran partagé
Comment DotsAnglais
LaptopOrdinateur de bureau uniquement

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.

Les compétences que vous développerez

Data ScienceMachine LearningPython ProgrammingSupport Vector Machine (SVM)Data Analysis

Apprendrez étape par étape

Votre enseignant(e) vous guidera étape par étape, grâce à une vidéo en écran partagé sur votre espace de travail :

  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

How Guided Projects work

Votre espace de travail est un bureau cloud situé dans votre navigateur, aucun téléchargement n'est requis.

Votre enseignant(e) vous guide étape par étape dans une vidéo en écran partagé

Enseignant

Foire Aux Questions

Foire Aux Questions

  • By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

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  • Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

  • Vous apprenez en effectuant des tâches dans un environnement à écran partagé, directement dans votre navigateur. Sur le côté gauche de l'écran, vous terminez la tâche dans votre espace de travail. Sur le côté droit de l'écran, vous voyez un(e) enseignant(e) qui vous guide tout au long du projet, étape par étape.

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