This is a course for data scientists, data analysts, engineers, researchers, software developers, and quants.
Prior experience with Python. High-school math knowledge is recommended. A quantitative background and familiarity with basic probability and linear algebra would also be beneficial but are not required.
You may skip day 1 if you have recently completed either of Python Charmers' courses Python for Predictive Data Analytics or Python for Scientists and Engineers.
This course introduces machine learning using scikit-learn and deep learning using PyTorch
By the end of the course, you will understand the concepts of classical ML algorithms as well as neural networks, convolutional neural networks, and transformers, and you will have experience applying these in practice to develop and refine models for classification and regression across various domains.
Day 1 gives you a practical and comprehensive introduction to machine learning for powerfully inferring complex models from data, with examples from a range of industries, including time-series and spatial datasets:
Day 2 introduces the approach to machine learning known as “deep learning”, using neural networks trained with the PyTorch library on GPUs:
Day 3 describes some of the most promising recent architectural innovations in deep learning models. It then walks you through the theory and practice of refining existing models trained by others and gives you advice on how to refine and deploy models in production:
We are happy to offer on-the-spot problem-solving after each day of the training for you to ask one-on-one questions — whether about the course content and exercises or about specific problems you face in your work and how to solve them. If you would like us to prepare for this in advance, you are welcome to send us background info before the course.
Courses are conducted online via video meeting using Python Charmers' cloud notebook server for sharing code with the trainer(s).
Hardware: we recommend ≥ 8 GB of RAM and a webcam. Preferably also multiple screens and a quiet room (or headset mic).
Software: a modern browser: Chrome, Firefox, or Safari (not IE or Edge); and Zoom.
Coding: we have a cloud-based coding server that supports running code and sharing code with the trainer(s).
Most courses will run from 9:00 to roughly 17:00 (AEST) each day, with breaks of 50 minutes for lunch and 20 minutes each for morning and afternoon tea.
Certificate of completion:
We will provide you a certificate if you complete the course and successfully answer the majority of the exercise questions.
You will have access to all the course materials via the cloud server.
We will also send you a bound copy of the course notes, cheat sheets, and a USB stick containing the materials, exercise solutions, and further resources.