Implement Logistic Regression using Python and Numpy.
Apply Logistic Regression to solve binary classification problems.
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.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
A Mini Batch of Examples
Check Model Performance
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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.
Want to do a project in Logistic Regression. You are at the right spot Don't delay and take the course.
Easy to follow along, each step was made very clear, and I understood the justification behind steps.
I enjoyed it. Thank you. But helper functions could be explained more or given as a blog.
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