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Machine Learning with Javascript


 Machine Learning with Javascript

Udemy Course Machine Learning with Javascript | NED

js is a library for machine learning in JavaScript. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. See tutorials.

What you'll learn
  • Assemble machine learning algorithms from scratch!
  • Build interesting applications using Javascript and ML techniques
  • Understand how ML works without relying on mysterious libraries
  • Optimize your algorithms with advanced performance and memory usage profiling
  • Use the low-level features of Tensorflow JS to supercharge your algorithms
Requirements
  • Basic understanding of terminal and command line usage
  • Ability to read basic math equations
Description

If you're here, you already know the truth: Machine Learning is the future of everything.

In the coming years, there won't be a single industry in the world untouched by Machine Learning.  A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change.  You probably already use apps many times each day that rely upon Machine Learning techniques.  So why stay in the dark any longer?

There are many courses on Machine Learning already available.  I built this course to be the best introduction to the topic.  No subject is left untouched, and we never leave any area in the dark.  If you take this course, you will be prepared to enter and understand any sub-discipline in the world of Machine Learning.


A common question - Why Javascript?  I thought ML was all about Python and R?

The answer is simple - ML with Javascript is just plain easier to learn than with Python.  Although it is immensely popular, Python is an 'expressive' language, which is a code-word that means 'a confusing language'.  A single line of Python can contain a tremendous amount of functionality; this is great when you understand the language and the subject matter, but not so much when you're trying to learn a brand new topic.

Besides Javascript making ML easier to understand, it also opens new horizons for apps that you can build.  Rather than being limited to deploying Python code on the server for running your ML code, you can build single-page apps, or even browser extensions that run interesting algorithms, which can give you the possibility of developing a completely novel use case!
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