- Level Intermediate
- Duration 3 hours
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Offered by
About
Welcome to this project-based course on Linear Regression 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 and linear regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. 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.Auto Summary
Embark on a practical journey into the world of data science and artificial intelligence with the "Linear Regression with NumPy and Python" course offered by Coursera. This intermediate-level course is designed to provide hands-on experience in implementing linear regression using Python and the powerful NumPy library. Over the span of 180 minutes, you will delve into the essential concepts and techniques needed to apply linear regression models to real-world data. This project-based course emphasizes practical application, ensuring that you gain valuable skills that can be directly applied in various data analysis and machine learning tasks. Whether you are a data science enthusiast, a budding AI professional, or someone looking to enhance their programming expertise in Python, this course is tailored to help you achieve your goals. Best of all, the course is available for free, making it an accessible and valuable resource for anyone eager to advance their knowledge in the data science domain. Join us and take a significant step forward in your data science journey with expert guidance and hands-on practice.