This is the code repository for Hands-On Explainable AI (XAI) with Python, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
- Paperback: 454 pages
- ISBN-13: 9781800208131
- Date Of Publication: 31 July 2020
Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex.
Hands-On Explainable AI (XAI) with Python will enable you to work with specific hands-on machine learning Python projects strategically arranged to enhance your grip on AI results analysis. The analysis includes building models, interpreting results with visualizations, and integrating understandable AI reporting tools and different applications.
You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source explainable AI tools for Python that can be used throughout the machine learning project life-cycle.
You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting machine learning model visualizations into user explainable interfaces.
By the end of this artificial intelligence book, you will possess an in-depth understanding of the core concepts of explainable AI.
All of the code is organized into folders that are named chapter-wise, for example: Chapter01.
The code will look like the following:
# Train decision tree classifier
estimator = estimator.fit(X_train,y_train)
#Predict the response for the test dataset
print("prediction")
y_pred = estimator.predict(X_test)
print(y_pred)Check this file for the hardware and software requirements: technical_requirements.md