About AI Charmers
We design and deliver technical AI and machine learning training for companies, government organisations and research teams. We develop the course material for each audience rather than taking it off the shelf.
Our courses are written for the people who build these systems. That means engineers, data scientists, analysts and researchers who work in Python, train models, and answer for the results. Every course is hands-on, and you leave with code you have written yourself.
Our lead trainers have each worked in AI and machine learning for 15 years, across research, industry and open source. In that time the field's idea of the state of the art has changed several times. Over the same 15 years we have delivered more than 700 training courses to over 7,000 participants.
We have spent our careers applying these techniques. That lets us teach how a method works, when it is the wrong choice, and why, in plain English.
Participants give their trainers the highest scores in our feedback survey. Across 1,036 responses from both brands, 99.5% agreed the trainer had a firm grasp of the content, 99.1% that they explained it simply, and 99.5% that they answered questions well.
Our expertise
We teach machine learning, deep learning and large language models. Our lead trainers have each worked in these fields for 15 years and still practise what they teach.
Our approach
Technical courses in small groups, taught in Python. You write the code and train the models yourself, on real data, with a trainer on hand to help.
Our mission
To help professionals and organisations apply AI and machine learning well. That means knowing what these methods can do, and where they fall short.
Our clients
We have trained teams at Boeing, Cisco, Toyota and Optus, and at many Australian government agencies and universities.
Our training philosophy
Taught by practitioners
Your trainer has built and shipped these systems. They can answer the questions that come up when yours does not behave.
You write the code
Every course is hands-on in Python. You train the models yourself and leave with working code you understand.
When not to use a method
We teach how to run each technique, and how to recognise when it is the wrong choice.
Ongoing support
Post-course support and resources, for when you are back at your desk and applying the work to your own problems.
Our team
Our trainers are working practitioners in machine learning, deep learning, data science and technical training.
Dr Ed Schofield
Founder and lead trainer
Ed holds a PhD in machine learning from Imperial College London, and BA and MA (Hons) degrees in mathematics and computer science from Trinity College, University of Cambridge. He is the founder of Data Charmers in Australia and Singapore.
Ed has trained over 7,000 people in data science and machine learning, from dozens of organisations including Atlassian, Barclays, Cisco, CSIRO, Dolby, Harvard University, IMC, Singtel Optus, Oracle, Shell, Telstra, Toyota, Verizon and Westpac. He is a former release manager of SciPy, part of the scientific computing stack that machine learning in Python is built on. He speaks at conferences around the world.
Robert Layton
Senior trainer
Robert is a contributor to scikit-learn, the open source machine learning library, and the author of Data Mining with Python, published by Packt in 2016. He wrote the website learningtensorflow.com and sold it to Databricks in 2017.
Robert provides analysis, consultancy, research and development work to businesses in the government, financial and security sectors. He was previously a Research Fellow at the Internet Commerce Security Laboratory, Federation University Australia, investigating cybercrime analytics and data-mining algorithms for attribution and profiling. He writes regularly on data mining, and has presented at international conferences on data analytics and its applications.
Henry Walshaw
Senior trainer and GIS expert
Henry has 16 years of experience in GIS, spatial analysis and application development, mostly in natural resource management. His core expertise is developing and analysing large spatial datasets, mainly in Python. He is also practised at explaining that work to subject matter experts and the general public.
Henry has worked in federal and state government, at Geoscience Australia, the Victorian Government Department of Sustainability and Environment, and the Environmental Protection Agency. In the private sector he has worked as a consultant, a manager, and CTO of a startup. He is currently a senior developer at FloodMapp, an Australian company developing real-time flood alerting systems. With Python Charmers he has trained over 1,000 people, from organisations including AGL, the Bureau of Meteorology, CSIRO, ESRI, National Australia Bank and Telstra. He holds a Bachelor's degree in Computational Science.
Emeritus trainers
We thank these past trainers for their work with our clients.
Dr Juan Nunez-Iglesias
Emeritus trainer
Juan is co-author of the book Elegant SciPy, published by O'Reilly Media. He is a core developer of the scikit-image library, and has contributed to many other scientific Python libraries, including SciPy, NetworkX and Matplotlib. He has taught and presented at the SciPy conference in Austin, EuroSciPy, PyCon Australia, the Advanced Scientific Programming in Python summer school, and Software Carpentry workshops.
Juan is a research fellow at Monash University, with research interests in neuroscience and biological image analysis. Outside research, he has a particular interest in renewable energy and the environment.
Juan has a Bachelor's degree in Biomedical Science from the University of Melbourne. He holds an MSc in Statistics and a PhD in Computational Biology and Bioinformatics from the University of Southern California.
Errol Lloyd
Emeritus trainer
Errol's background is in computational neuroscience. He has been using Python for over a decade, to model neurological systems and for digital signal processing, data analysis and empirical research.
Before joining Python Charmers in 2020, Errol trained fellow researchers in the sciences and the humanities to solve research problems with software. He taught data and natural language analysis in Python, data visualisation, interactive dashboards with front-end JavaScript, and version control and collaboration with git and GitHub.
Errol advocates open source software and reproducible research in science, and wants to help more people use code to get their work done. He completed doctoral studies on visual processing in the brain at the University of Melbourne.
Dr Clare Sloggett
Emeritus trainer
Clare started in computational physics and moved into genomics. Her research interests include data visualisation and algorithms for genomic data, and her current focus is machine learning for clinical and genomic data. She co-organised the Python in Science and Data Miniconf at PyCon AU from 2015 to 2017. She regularly speaks at conferences and community events on genomics and on data analytics with Python and other open source tools.
Clare holds a BSc and PhD in computational physics from the University of NSW.
Bianca Gibson
Emeritus trainer
Bianca is a development team lead at Infoxchange, an Australian charity. She uses Python to build a directory that connects people in need with housing, a meal, money help, family violence support, counselling and other services. She has been using Python for over a decade, and has worked mostly in the government and not-for-profit sectors. She trained her colleagues at realestate.com.au and the Australian Bureau of Meteorology in Python and development tools. She has presented three times at PyCon AU and RubyConf AU, on topics including functional programming, and an analysis of "imposter syndrome" and fostering healthy self-belief.
Bianca has an undergraduate degree in computer science from Monash University, Australia.
Dr Ned Letcher
Emeritus trainer
Ned is a data scientist and software engineer. He has helped a range of organisations with projects in machine learning, natural language processing, information retrieval and data visualisation. He has been using Python for data analysis, visualisation, machine learning and web development for over 10 years. Ned contributes to the Plotly Dash library and is an active member of the Dash community.
Ned has a PhD in computational linguistics from the Natural Language Processing group at the University of Melbourne. He also has a Bachelor of Arts (philosophy and linguistics) and a Bachelor of Science with Honours (computer science). Ned regularly presents at local meetups, and organises the Melbourne Data Visualisation Meetup.
Testimonials
What participants say about our courses
The course was delivered by trainers who were extremely knowledgeable in their field. It was really good to learn from the best!
Great course. Enjoyed it and will follow up with some practical implementation of some of the work.
One of the best training courses I've been on.
Find a course
Browse the course list, or tell us what your team needs and we will suggest a course or design one.