Tutorial de regresión logística con Python y Numpy

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En este Tutorial guiado, tú:

Implement Logistic Regression using Python and Numpy.

Apply Logistic Regression to solve binary classification problems.

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

Welcome to this guided tutorial on Logistic Regression with NumPy and Python. In this tutorial, 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 tutorial is to implement all the machinery, including gradient descent, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this tutorial, you will be able to build a logistic regression model using Python and NumPy, conduct basic exploratory data analysis, and implement gradient descent from scratch. The prerequisites for this tutorial are prior programming experience in Python and a basic understanding of machine learning theory. This tutorial runs on Coursera's hands-on platform called Rhyme. On Rhyme, you do tutorials 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 tutorial. Everything is already set up directly in your internet browser so you can just focus on learning. For this tutorial, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed.

Habilidades que desarrollarás

Deep LearningMachine LearningLogistic RegressionPython ProgrammingNumpy

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. Hyperparameters

  3. Dataset

  4. A Mini Batch of Examples

  5. Create Model

  6. Forward Pass

  7. Backward Pass

  8. Update Parameters

  9. Check Model Performance

  10. Training Loop

Cómo funcionan los tutoriales 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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