Machine Learning Maths and Modelling
Key concepts for Machine Learning
Scope of the Course
Gain a solid understanding of some of the concepts behind commonly used machine learning methods and the statistical concepts – including avoiding fallacies - needed for framing problems and formulating questions.
Who is the intended audience for the course?
People who need to quickly come up to speed with how machine learning works and quickly implement new practical skills.
What is the need for this course, and why will participants benefit from it?
Behind the application of AI the tasks involved in problem definition, solution framing and explaining outcomes require human skills and judgement. These skills are a long way off from being automated and are therefore essential to any organisation wishing to implement AI. These skills are also rare because of the time it takes to learn them which is why this course has been designed with the goal of rapid, practical learning
Participants will learn the mechanics of machine learning so that that AI can be optimally use in their organisation. Topics include:
- Python primer
- Essential statistics
- How to frame machine learning problems
- Problem categorisation
- Visualising data
- Data wrangling
- Machine learning models
- Machine learning processes
The main Machine Learning methods that we'll work with are:
- Random Forest
- K-means Clustering
What are the learning outcomes?
At the end of this course, you will have acquired the following:
- Recognise opportunities for applying machine learning
- Use key machine learning methods
- Apply machine learning to practical problems
- Understand the benefits and limitations of machine learning
- Key mathematical, statistical and coding concepts
- Machine learning processes
- Analyse and evaluate what machine learning can and can’t do
- Be able to frame machine learning problems opportunities
- Guide the development of machine learning solutions
- A development ‘sandbox' that you can use and customise after the course
- Starter code and data
- Data and model visualisation
Mike’s passion is to simplify Deep Tech to enable organisations grow, and help people to learn.
In September 2013, after a long and successful career at Microsoft, Mike founded what was to become learn-tech.io - a company dedicated to democratising Deep Tech, and helping people develop technical, scientific, mathematics, engineering and business knowledge and skills. learn-tech.io now has customers for its products and services in UK, Singapore, China and Australia.
Mike started his career in automotive engineering, spent 10 years teaching and at Secondary and University levels, then worked at Microsoft for 13 years before founding his current business. His experience spans the full spectrum of technology - from AI to Zero Emissions solutions.
Learn-tech.io provides custom learning and technology solutions to organisations such as Pearson, Intel, Microsoft, ARM, BBC, Singapore Science Centre, RM Plc, Box Hill Institute, Burges Salmon and the Bristol Technology and Engineering Academy.
The learn-tech.io organisation includes partners in Asia, and “Deep Tech Task Force” - a team of interns focussed on simplifying a wide range of aspects of Deep Tech from Neural Networks, to FinTech, to Law and Public Policy.
Mike is the author of the “AI Demystified” and “How to Make a Mind?” courses. He has delivered keynote speeches across the world on a range of technology related subjects and has worked at senior level in over 30 countries.