Support Vector Machines with scikit-learn
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
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 desarrollarás
- Data Science
- Machine Learning
- Python Programming
- Support Vector Machine (SVM)
- Data Analysis
Aprende paso a paso
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Getting Started
Beyond Linear Discriminative Classifiers
Many Possible Separators
Plotting the Margins
Training an SVM Model
Facial Recognition with SVMs
Preprocessing the data set
Hyperparameter Tuning with Grid-Search Cross Validation
Visualize Test Images
Evaluating the Support Vector Classifier
Cómo funcionan los proyectos guiados
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
Reseñas
Principales reseñas sobre SUPPORT VECTOR MACHINES WITH SCIKIT-LEARN
It might be difficult for some people to understand this course who have zero knowledge of machine learning. Overall the course was good.
Beginner friendly and walks you through most of major steps which are usually done in Machine Learning Projects with SVM. Good course
Learned about SVM. Need t revisit the code and get most out of it.\n\nThings were concise and that is the strength of the course.
This guided project will definitely give you a practical approach to what you have read in SVM. Will definitely worth your time.
Preguntas Frecuentes
Preguntas Frecuentes
¿Qué obtendré si compro un proyecto guiado?
¿Hay proyectos guiados disponibles en computadoras de escritorio y dispositivos móviles?
¿Quiénes son los instructores para proyectos guiados?
¿Puedo descargar el trabajo de mi proyecto guiado después de completarlo?
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
¿Puedo participar como oyente de un proyecto guiado y mirar el video de forma gratuita?
¿Cuánta experiencia necesito para hacer este proyecto guiado?
¿Puedo completar este proyecto guiado directamente a través de mi navegador web, en lugar de instalar un software especial?
¿Cómo es la experiencia de aprendizaje con los proyectos guiados?
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