Part 2: Practical Machine Learning with LightGBM

Part 2 delves into the intricate processes that underpin practical machine learning engineering, starting with a look at efficient hyperparameter optimization via a framework called Optuna. We will then transition into a comprehensive exploration of the data science lifecycle, illustrating the rigorous steps from problem definition and data handling to practical data science modeling applications. Concluding this part, the focus will shift to automated machine learning, spotlighting the FLAML library, which aims to simplify and streamline model selection and tuning. Throughout this part, a blend of case studies and hands-on examples will provide a clear roadmap to harnessing the full potential of these advanced tools, underscoring the themes of efficiency and optimization.

This part will include the following chapters:

• Chapter 5, LightGBM Parameter Optimization with Optuna
• Chapter 6, Solving Real-World Data Science Problems...