Support Vector Machine Classification in Python
import the dataset and perform training/testing set splits
Apply feature scaling for normalization
Build an SVM classifier and make Predictions
Build a Confusion Matrix and Visualize the results
In this 1-hour long guided project-based course, you will learn how to use Python to implement a Support Vector Machine algorithm for classification. This type of algorithm classifies output data and makes predictions. The output of this model is a set of visualized scattered plots separated with a straight line. You will learn the fundamental theory and practical illustrations behind Support Vector Machines and learn to fit, examine, and utilize supervised Classification models using SVM to classify data, using Python. We will walk you step-by-step into Machine Learning supervised problems. With every task in this project, you will expand your knowledge, develop new skills, and broaden your experience in Machine Learning. Particularly, you will build a Support Vector Machine algorithm, and by the end of this project, you will be able to build your own SVM classification model with amazing visualization. In order to be successful in this project, you should just know the basics of Python and classification algorithms.
Habilidades que desarrollarás
- Machine Learning
- Python Programming
- Support Vector Machine (SVM)
- classification
- Supervised Learning
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:
Understand the concept of building a Support Vector Machine classification algorithm with a real-world example
Import and explore the dataset and libraries: numpy, pandas and matplotlib
Split the dataset into training set and testing set
Apply feature scaling to normalize the input features
Fit the SVM classifier to the dataset and making predictions
Visualize training and testing sets results
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 MACHINE CLASSIFICATION IN PYTHON
Straight to the point, take a little bit of time and it is very useful for anyone seeking more knowledge in this domain. Thumbs up to the instructor.
it was a little bit hard to get message, when he did not write the code for visualizing and copied it. My code never get executed even I copied.
Good Course. But voice of instructor was not clear enough and he was very slow. Overall, I have learned some new things. Thanks Instructor.
Gave me a good intuition for applying SVM classifier in python as well as visualising predictions, thanks for guiding me through this.
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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