Practical Machine Learning Theory

Learn enough to use your ML tools properly

What you’ll learn
Understand if your Machine Learning model is profitable or trash
Understand the process of training and validating a Machine Learning model at a high level
Understand the error metrics of Machine Learning models
Be able to choose the right AotoML API or the right drag-and-drop box for the job that you are doing

Requirements
An understanding of business is desirable but not essential

Description
A long time ago, data scientists wrote code to train machine learning models. They connected open source libraries, to create a bespoke solution for every project. They had to know the math. All of the use cases were new.Now there are a lot of cloud services that will train a Machine Learning model – without the user having ML expertise. But you still to understand the inputs, and the outputs. You still need to choose the right API for the job. You need to understand if the outputs are profitable – or trash. That’s what you will learn in this course.My name is Slava Razbash, and I’ve been working in Machine Learning roles since 2011. I have worked in some of Australia’s largest companies, including Commonwealth Bank of Australia (Australia’s largest bank), Sportsbet (Flutter Entertainment), Tabcorp (multinational gambling business), Coles (one of Australia’s two largest supermarket chains) and AGL (one of Australia’s largest energy companies). If you are a developer who uses AutoML APIs, or a business user who uses drag-and-drop no-code ML tools. Then you need this course. Because are just mashing the keyboard without it.Enrol in the course now, and level up the quality of your work.

Developers who use AutoML API,Business users who use dran-and-drop no-code Machine Learning tools

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