- Assemble machine learning algorithms from scratch!
- 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
- Basic understanding of terminal and command line usage
- Ability to read basic math equations
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.
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
- Data loading techniques, both in the browser and Node JS environments
Created by Stephen Grider
Last updated 4/2020
Size: 10.10 GB