Machine Learning Foundation is a three-day, instructor-led course that helps tech professionals put data to work through machine learning. It covers the principles of machine learning, model development and evaluation, and techniques for processing and understanding data. Attendees get hands-on experience with toolkits such as PyTorch, Scikit-Learn, Polars, NumPy, and Plotly, building skills they can apply directly to real projects.
Day one moves from a machine learning overview through hands-on data processing with NumPy and Pandas. It then continues into a dedicated data preparation module before visualization and exploratory data analysis. Day two starts by measuring models with the losses and metrics that guide every algorithm choice. It then contrasts linear models against nonlinear supervised and unsupervised approaches. Day three opens with hyperparameter tuning and experimentation before covering deep learning fundamentals and how neural networks train. The day closes with attendees building their own deep learning models in PyTorch. Industry veterans share lessons from real projects throughout, so the algorithms taught connect to how production teams actually apply them.
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 Foundation, participants will be able to:
- Understand the fundamental concepts of machine learning and how they’re applied to provide value
- Learn how to develop, tune and test machine learning models
- Become familiar with tools for machine learning and data analysis
- Gain insight into how production grade machine learning solutions are developed
- Acquire hands-on experience building models and solving problems with ML
- Learn insights from industry veterans about how to develop successful ML projects
- Develop the skills and understanding to develop deep learning solutions
Prerequisites
Participants should have beginner programming skills (preferably in Python) and a foundational understanding of mathematics and statistics.