Machine Learning with Javascript

4.7 (2056)
MOOC
Payment
Learning paid
Language
English
Duration
17.5 hours course
Course by Udemy
What will you 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
Grow a strong intuition of ML best practices
About the course

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!

Does this course focus on algorithms, or math, or Tensorflow, or what?!?!

Let's be honest - the vast majority of ML courses available online dance around the confusing topics.  They encourage you to use pre-build algorithms and functions that do all the heavy lifting for you.  Although this can lead you to quick successes, in the end it will hamper your ability to understand ML.  You can only understand how to apply ML techniques if you understand the underlying algorithms.

That's the goal of this course - I want you to understand the exact math and programming techniques that are used in the most common ML algorithms.  Once you have this knowledge, you can easily pick up new algorithms on the fly, and build far more interesting projects and applications than other engineers who only understand how to hand data to a magic library.

Don't have a background in math?  That's OK! I take special care to make sure that no lecture gets too far into 'mathy' topics without giving a proper introduction to what is going on.

A short list of what you will learn:

  • Advanced memory profiling to enhance the performance of your algorithms
  • Build apps powered by the powerful Tensorflow JS library
  • Develop programs that work either in the browser or with Node JS
  • Write clean, easy to understand ML code, no one-name variables or confusing functions
  • Pick up the basics of Linear Algebra so you can dramatically speed up your code with matrix-based operations. (Don't worry, I'll make the math easy!)
  • Comprehend how to twist common algorithms to fit your unique use cases
  • Plot the results of your analysis using a custom-build graphing library
  • Learn performance-enhancing strategies that can be applied to any type of Javascript code
  • Data loading techniques, both in the browser and Node JS environments
Program
What is Machine Learning?
Getting Started - How to Get Help
Solving Machine Learning Problems
A Complete Walkthrough
App Setup
Problem Outline
Identifying Relevant Data
Dataset Structures
Recording Observation Data
What Type of Problem?
Algorithm Overview
How K-Nearest Neighbor Works
Requirements
  • Basic understanding of terminal and command line usage
  • Ability to read basic math equations
Lecturers
Stephen Grider
Stephen Grider
Engineering Architect
Platform
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Udemy courses are suited to professional development. The platform is organized in such a way that it is experts themselves that decide the topic and when the course will start. All supporting documents are made available to you for lifetime access. On this platform, you can find a course on about any subject, and that is no exaggeration – from a tutorial on how to ride a motorcycle, to managing the financial markets. The language and the course format are established by the teacher. This is why it is important to read the information about the course carefully before parting with any money.
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