Principal Component Analysis with NumPy
Welcome to this 2 hour long project-based course on Principal Component Analysis 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 of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals.
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- Level Intermediate
- Duration 3 hours
- Course by Coursera Project Network
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Welcome to this 2 hour long project-based course on Principal Component Analysis 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 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 implement and apply PCA from scratch using NumPy in Python, conduct basic exploratory data analysis, and create simple data visualizations with Seaborn and Matplotlib. 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.Modules
Principal Component Analysis with NumPy
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Assignment
- Graded Quiz: Test Your Project Understanding
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Labs
- Principal Component Analysis with NumPy
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- Project-based Course Overview
Auto Summary
Dive into the fundamentals of Principal Component Analysis (PCA) with a hands-on approach in the course "Principal Component Analysis with NumPy." This 2-hour project-based course, tailored for those with prior Python programming experience and a basic understanding of machine learning, focuses on implementing machine learning algorithms from scratch without relying on popular libraries like scikit-learn or statsmodels. Guided by Coursera, you'll learn to apply PCA using NumPy in Python, conduct exploratory data analysis, and create data visualizations with Seaborn and Matplotlib. The course is delivered through Coursera's Rhyme platform, providing instant access to a pre-configured cloud desktop with all necessary software, enabling you to immerse yourself in the learning experience directly through your browser. Ideal for intermediate learners, this free course offers a comprehensive and practical understanding of PCA, ensuring you gain the skills to implement and apply this critical machine learning technique effectively.
Snehan Kekre