Linear Regression with NumPy and Python

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En este proyecto guiado, tú:

Implement the gradient descent algorithm from scratch

Perform univariate linear regression with Numpy and Python

Create data visualizations and plots using matplotlib

Clock1.5 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

Welcome to this project-based course on Linear Regression with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. 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, NumPy, and Seaborn pre-installed.

Habilidades que desarrollarás

Data ScienceMachine LearningPython ProgrammingregressionNumpy

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 and Overview

  2. Load the Data and Libraries

  3. Visualize the Data

  4. Compute the Cost Function 𝐽(𝜃)

  5. Gradient Descent

  6. Visualize the Cost Function 𝐽(𝜃)

  7. Plot the Convergence

  8. Training Data with Univariate Linear Regression Fit

  9. Inference using the optimized 𝜃 values

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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