Logistic Regression with Python and Numpy
6035 ya inscrito
6035 ya inscrito
Welcome to this project-based course on Logistic 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, 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 project, 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 project are prior programming experience in Python and a basic understanding of machine learning theory. 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.
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
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
por MK19 de jul. de 2020
I enjoyed it. Thank you. But helper functions could be explained more or given as a blog.
por DP8 de abr. de 2020
Want to do a project in Logistic Regression. You are at the right spot Don't delay and take the course.
por BA26 de sep. de 2020
Well..I would like to recommend this project for machine learning students who can have a better understanding of concepts related to deep learning and Ml.
por ST8 de mar. de 2020
it is a great course and successfully trained my ml model