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Implement Logistic Regression using Python and Numpy.
Apply Logistic Regression to solve binary classification problems.
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.
Deep Learning
Machine Learning
Logistic Regression
Python Programming
Numpy
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction
Hyperparameters
Dataset
A Mini Batch of Examples
Create Model
Forward Pass
Backward Pass
Update Parameters
Check Model Performance
Training Loop
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
by ST
Mar 8, 2020it is a great course and successfully trained my ml model
by MT
Mar 9, 2020Easy to follow along, each step was made very clear, and I understood the justification behind steps.
by MK
Jul 19, 2020I enjoyed it. Thank you. But helper functions could be explained more or given as a blog.
by BA
Sep 26, 2020Well..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.
By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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