RX-M has partnered with the The Linux Foundation to offer Machine Learning & AI Introduction (RXM402) to the community.
Machine Learning & AI Introduction shows tech professionals how to put modern machine learning tools to work with real data. The course fits into a single day. It covers the principles of AI and machine learning, model development, and data visualization, along with techniques for processing and understanding data. It moves from an AI/ML overview and data processing through visualization, building models, and hyperparameter tuning. Attendees get hands-on experience with libraries like Pandas, NumPy, Seaborn, scikit-learn, and Optuna.
The opening lab has attendees set up a working machine learning environment. They then clean and process sample data with NumPy and Pandas before touching a model. Visualization work pairs Seaborn with Matplotlib, so participants can chart distributions and relationships before selecting features. The modeling lab moves into scikit-learn. Attendees train and compare several algorithms on the same dataset. The closing lab tunes those models with Optuna to see how systematic hyperparameter search improves results over manual guessing.
The audience spans developers, data engineers, IT and QA staff, DevOps engineers, analysts, project managers, and technical managers. The pace balances hands-on coding for builders with enough context for those steering AI/ML projects rather than writing the code themselves. The course is delivered in person or online in a single day. The format keeps lecture segments short so each of the four labs gets real time on the keyboard.
Who Should Attend
Developers, Data Engineers, IT and QA Staff, Technical Managers, DevOps Engineers, Analysts, Project Managers
What Attendees Will Learn
Upon completing Machine Learning & AI Introduction, participants will be able to:
- Understand the fundamental concepts of AI/Machine Learning and how they’re applied to provide business value
- Develop, tune and test machine learning models
- Work with tools for machine learning and data analysis
- Understand how production-grade AI/Machine Learning solutions are developed
- Gain hands-on experience building models and solving problems with AI and Machine Learning
Prerequisites
Participants should have basic programming skills (preferably in Python), a foundational understanding of mathematics and statistics, and some experience with data analysis.