Part 2:Improving Machine Learning Models

This part will help us transition into the critical aspects of refining and understanding machine learning models. We will start with a deep dive into detecting performance and efficiency bottlenecks in models, followed by actionable strategies to enhance their performance. The narrative then shifts to the subject of interpretability and explainability, elucidating the importance of not just building models that work, but ones we can understand and trust. We will conclude this part by presenting the methods to reduce bias, emphasizing the imperative of fairness in machine learning.

This part has the following chapters:

• Chapter 4, Detecting Performance and Efficiency Issues in Machine Learning Models
• Chapter 5, Improving the Performance of Machine Learning Models
• Chapter 6, Interpretability and Explainability in Machine Learning Modeling
• Chapter 7, Decreasing Bias and Achieving Fairness

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