Practical Explainable AI Using Python

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"Practical Explainable AI Using Python" is a comprehensive guide to building and interpreting machine learning models using Python. This book is written by Dr. Sameer Singh, a highly experienced data scientist who has worked with some of the world's leading technology companies.

The book starts with an introduction to the concept of explainable AI and its importance in building transparent and trustworthy AI systems. It then goes on to cover the basics of Python programming and the various machine learning libraries available in Python.

The book provides a step-by-step approach to building and evaluating machine learning models using Python, with a focus on explainability. It covers popular algorithms such as decision trees, random forests, and support vector machines, and demonstrates how to interpret the results of these models using techniques such as feature importance and partial dependence plots.

In addition to building models, the book also covers techniques for evaluating and improving model performance, such as cross-validation and hyperparameter tuning. It also provides practical guidance on how to implement and deploy machine learning models in real-world scenarios.

What sets this book apart is its focus on explainability. The author emphasizes the importance of building models that can be understood and interpreted by humans, rather than just optimizing for predictive accuracy. This makes the book an invaluable resource for anyone who wants to build transparent and trustworthy AI systems.

Overall, "Practical Explainable AI Using Python" is a must-read for data scientists, machine learning engineers, and anyone interested in building explainable AI systems using Python. It provides a practical, hands-on approach to building and interpreting machine learning models, and is filled with real-world examples and best practices.


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