Machine Learning Principal Component Analysis in Python
Last updated 12/2022
Duration: 2h 5m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 883 MB
Genre: eLearning | Language: English
Learn to apply Principal Component Analysis in Python from a Data Science expert. Code templates included.
What you'll learn
Master Principal Component Analysis in Python
Become an advanced, confident, and modern data scientist from scratch
Become job-ready by understanding how PCA really works behind the scenes
Apply robust Machine Learning techniques for Principal Component Analysis
How to think and work like a data scientist: problem-solving, researching, workflows
Get fast and friendly support in the Q&A area
Requirements
No data science experience is necessary to take this course.
Any computer and OS will work — Windows, macOS or Linux. We will set up your code environment in the course.
Description
You've just stumbled upon the most complete, in-depth Principal Component Analysis course online.
Whether you want to
- build the skills you need to get your first data science job
- move to a more senior software developer position
- become a computer scientist mastering in data science
- or just learn PCA to be able to create your own projects quickly.
...this complete Principal Component Analysis Masterclass is the course you need to do all of this, and more.
This course is designed to give you the PCA skills you need to become a data science expert. By the end of the course, you will understand the PCA technique extremely well and be able to apply it in your own data science projects and be productive as a computer scientist and developer.
What makes this course a bestseller?
Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.
Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That's why so many find success in this complete Principal Component Analysis course. It's designed with simplicity and seamless progression in mind through its content.
This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the PCA technique. It's a one-stop shop to learn PCA. If you want to go beyond the core content you can do so at any time.
What if I have questions?
As if this course wasn't complete enough, I offer full support, answering any questions you have.
This means you'll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you'll progress smoothly through this course without any major roadblocks.
There's no risk either!
This course comes with a guarantee. Meaning if you are not completely satisfied with the course or your progress, simply let me know and I'll refund you 100%, every last penny no questions asked.
You either end up with Haskell skills, go on to develop great programs and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don't like it.
You literally can't lose.
Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.
And as a bonus, this course includes Python code templates which you can download and use on your own projects.
Ready to get started, developer?
Enroll now using the "Add to Cart" button on the right, and get started on your way to creative, advanced PCA brilliance. Or, take this course for a free spin using the preview feature, so you know you're 100% certain this course is for you.
See you on the inside (hurry, PCA is waiting!)
Who this course is for
Any people who want to start learning PCA in Machine Learning
Anyone interested in Machine Learning
Anyone who want to understand how to apply PCA in datasets using Python
Homepage
https://www.udemy.com/course/machine-learning-principal-component-analysis-in-python/
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