Logistic Regression with Python and Numpy
5,940명이 이미 등록했습니다.
5,940명이 이미 등록했습니다.
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.
작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.
작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.
분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.
MK 제공2020년 7월 19일
I enjoyed it. Thank you. But helper functions could be explained more or given as a blog.
MT 제공2020년 3월 9일
Easy to follow along, each step was made very clear, and I understood the justification behind steps.
BA 제공2020년 9월 26일
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.
DP 제공2020년 4월 8일
Want to do a project in Logistic Regression. You are at the right spot Don't delay and take the course.