This module is designed to quickly give you the tools you need to understand how to frame AI in terms of how it works and how you can put it to use.

By the end of this module, you'll be able to recognise what kind of problems are and aren't solvable with AI, and the main approaches, models are for turning data into actionable predictions.

You will –

Explore

Work with some quick and easy online demos to get a feel for the capabilities of AI

Build

Take data, and apply machine learning code to it

Practice

Learn how models work by changing parameters in code

You will work with Python which is becoming a 'must have' tool for Data Science. Python is easy and fast to learn and 0-30 lines of Python code can validate most machine learning ideas. Python code can be incorporated into General Purpose Coding programs which means that you can build enterprise solutions which include python components. It also has many AI and data science libraries.

Your journey into machine learning will start with simplified facial recognition example which will lead you into an understanding the four main machine learning models.

The role of data is pivotal in AI. No data, no AI. So next you will learn how to work with different kinds of data, the different formats that it can come in, how to prepare it and how to learn from it.

Once you have a good understanding of the role of data in machine learning you can start to work with data in the sandbox.

By this stage you will have learned enough background to start working with regression and classification machine learning models. You'll model credit scoring and conduct image and word classification.

In the last section we will see how neural networks work.

We trust that you will greatly enjoy this practical journey through AI and machine learning - let's go!