Book Image

Applied Machine Learning Explainability Techniques

By : Aditya Bhattacharya
Book Image

Applied Machine Learning Explainability Techniques

By: Aditya Bhattacharya

Overview of this book

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases. Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users. By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.
Table of Contents (16 chapters)
1
Section 1 – Conceptual Exposure
5
Section 2 – Practical Problem Solving
12
Section 3 –Taking XAI to the Next Level

Technical requirements

The primary goal of this chapter is to provide a conceptual understanding of the model explainability methods. However, I will provide certain tutorial examples to implement some of these methods in Python on certain interesting datasets. We will be using Python Jupyter notebooks to run the code and visualize the output throughout this book. The code and dataset resources for Chapter 2 can be downloaded or cloned from the following GitHub repository: https://github.com/PacktPublishing/Applied-Machine-Learning-Explainability-Techniques/tree/main/Chapter02. Other important Python frameworks that are required to run the code will be mentioned in the notebooks along with other relevant details to understand the code implementations within these concepts.