Practical Machine Learning
The course is part of this learning path
Machine learning is a big topic. Before you can start to use it, you need to understand what it is, and what it is and isn’t capable of. In this module we’ll start with the basics, introducing you to AI and its history. We’ll discuss the ethics of it, and talk about examples of currently existing AI. We’ll cover Data, statistics and variables, before moving onto notation, supervised and unsupervised learning. Finally, we’ll end off by going into some depth on the theoretical basis for machine learning, model and linear regression, the semantic gap and how we approximate the truth.
- Let's talk about data. What does this word mean? Well, it's used in all kinds of contexts today, but I think in a technical or practitioner context we mean specifically information which can be used by computers. Now in the context of machine learning, we are interested in understanding data as composed of columns, or what might be called variables. So, data here is always tabular in our model, in our thinking about the problem. So we have some variables that say X1, X2, X3, and we have a Y, and we are thinking in terms of a tabular layout, and what we mean by a variable then is such a column, or variable, and here we use the letter X to denote a feature or what you might call an observation, an example, a trait or characteristic, something else. So here Y we call the target and that is the thing that we are trying to predict. So prediction target. Now there are different kinds of connection that Y may have to X. In general, we call such connections functions or relationships and the machine using statistics is able to determine from a historical data set, and then by using such a formula able to, in the future, estimate or predict something for Y given what it can see for X.
About the Author
Michael began programming as a young child, and after freelancing as a teenager, he joined and ran a web start-up during university. Around studying physics and after graduating, he worked as an IT contractor: first in telecoms in 2011 on a cloud digital transformation project; then variously as an interim CTO, Technical Project Manager, Technical Architect and Developer for agile start-ups and multinationals.
His academic work on Machine Learning and Quantum Computation furthered an interest he now pursues as QA's Principal Technologist for Machine Learning. Joining QA in 2015, he authors and teaches programmes on computer science, mathematics and artificial intelligence; and co-owns the data science curriculum at QA.