Logistic Regression with NumPy and Python
Welcome to the Logistic with NumPy and Python project-based course. You will accomplish all of the machine learning in this project without utilizing any of the popular machine learning libraries like scikit-learn or statsmodels. The goal of this project is for you to implement all of the machinery of the various learning algorithms yourself, including gradient descent, cost function, and logistic regression, so you may gain a better knowledge of the principles. You will be able to design a logistic regression model using Python and NumPy, do basic exploratory data analysis, and develop gradient descent from scratch after completing this project. Prior Python programming skills and a basic understanding of machine learning theory are required for this project.
This course uses Rhyme, Coursera's hands-on project platform. Rhyme allows you to work on projects in your browser in a hands-on manner. You'll have immediate access to pre-configured cloud PCs with all of the applications and data you'll need for the job. Everything is fully set up in your browser, so you can concentrate solely on learning. You'll gain immediate access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed for this project
THE SKILLS YOU WILL DEVELOP
- Data science
- Machine-learning
- Python Programming
- Classification
- Numpy
LEARN STEP BY STEP:
- Introduction and Project Overview
- Load the Data and Import Libraries
- Visualize the Data
- Define the Logistic Sigmoid Function
- Compute the Cost Function and Gradient
- Cost and Gradient at Initialization
- Implement Gradient Descent
- Plotting the Convergence of
- Plotting the Decision Boundary
- Predictions Using the Optimized Values
Rating: 4.5/5
Enroll here: coursera.org/projects/logistic-regression-numpy-python
