Introduction to Machine Learning with Python (Part 1)
Who is this for?
Students, developers, and professionals who want a practical introduction to Machine Learning with Python, without the hype, just practical explanations and hands-on coding.
If you’re confused by ML being explained with buzzwords or abstract theory, this session gives you the Python fundamentals you actually need to build and use basic machine learning models from scratch.
Who is leading the session?
The session is led by Dr. Stelios Sotiriadis, CEO of Warestack, Associate Professor and MSc Programme Director at Birkbeck, University of London, specialising in cloud computing, distributed systems, and AI engineering.
Stelios holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has worked on industry and research projects with Huawei, IBM, Autodesk, and multiple startups. Since moving to London in 2018, he has been teaching at Birkbeck. In 2021, he founded Warestack, building software for startups around the world.
What we’ll cover
A practical introduction to core machine learning concepts and how to implement them with Python and scikit-learn:
The fundamentals of machine learning
Understanding datasets, features, and target variables
Data preprocessing and normalization
Training common ML models for classification and regression using Python libraries.
Evaluating models for accuracy
Visualising results with Python
Hands-on examples you can run directly in Google Colab
This session focuses on real code, clear understanding, and practical ML engineering.
What are the requirements?
Bring a laptop and ensure you have a Gmail account.
The session will run entirely on Google Colab, so no local installation is required.
Format
A 2-hour live hands-on class, structured around:
Interactive explanations
Guided coding
Step-by-step exercises
Mini challenges
Q&A
This is a practical, code-first session, suitable for both beginners and intermediate Python users wanting to level up.
In-person or online?
The class will run in person, with streaming available for remote attendees.
Please note:
In-person participation is strongly preferred, as the session includes hands-on coding, live troubleshooting, and personalised support that cannot be fully provided to remote participants.
Prerequisites
You should be comfortable writing basic Python scripts (variables, loops, functions, imports). No prior machine learning experience is required.
A link will be shared to participants after registration.
Are there going to be more sessions?
Yes, this is the first session in a new series on practical Machine Learning and applied AI with Python. Additional sessions will be scheduled afterwards, covering further machine learning and AI algorithms.
What comes after?
Participants will receive an optional mini ML assignment and recommended next steps for deeper learning.