Linear Regression with Python

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En este proyecto guiado, tú:

Create a linear model, and implement gradient descent.

Train the linear model to fit given data using gradient descent.

Clock2 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

In this 2-hour long project-based course, you will learn how to implement Linear Regression using Python and Numpy. Linear Regression is an important, fundamental concept if you want break into Machine Learning and Deep Learning. Even though popular machine learning frameworks have implementations of linear regression available, it's still a great idea to learn to implement it on your own to understand the mechanics of optimization algorithm, and the training process. Since this is a practical, project-based course, you will need to have a theoretical understanding of linear regression, and gradient descent. We will focus on the practical aspect of implementing linear regression with gradient descent, but not on the theoretical aspect. Note: 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 ScienceDeep LearningMachine LearningPython ProgrammingLinear Regression

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:

  1. Introduction

  2. Dataset

  3. Initialize Parameters

  4. Forward Pass

  5. Compute Loss

  6. Backward Pass

  7. Update Parameters

  8. Training Loop

  9. Predictions

  10. Additional Example

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

Instructor

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