This course provides a practical framework for selecting ML algorithms based on your problem type, data characteristics, interpretability requirements, performance goals, available resources, training time, and business context. You’ll start by learning how to identify the type of ML problem you’re solving and evaluate important factors such as data size and quality. You’ll then explore the trade-off between model performance and interpretability, including when highly interpretable models are preferable and when high-performance, less interpretable models may be appropriate.
The course also teaches you how data characteristics drive algorithm selection. You’ll examine data size, linearity, dimensionality, and the curse of dimensionality, and learn how to match different data profiles to appropriate ML models. Real-world case studies and a Python mini-demo will help connect these concepts to practical decision-making. Finally, you’ll explore scalability and big data, including training time, computational complexity, training strategies, horizontal and vertical scaling, and techniques for reducing training time. A simplified Python mini-demo demonstrates mini-batch training.
