art 1:Machine Learning Landscape in Software Engineering Machine Learning Compared to Traditional Software Elements of a Machine Learning System Data in Software Systems ？ Text, Images, Code, and Their Annotations Data Acquisition, Data Quality, and Noise Quantifying and Improving Data Properties Part 2: Data Acquisition and Management Processing Data in Machine Learning Systems Feature Engineering for Numerical and Image Data Feature Engineering for Natural Language Data Part 3: Design and Development of ML Systems Types of Machine Learning Systems ？ Feature-Based and Raw Data-Based (Deep Learning) Training and Evaluating Classical Machine Learning Systems and Neural Networks Training and Evaluation of Advanced ML Algorithms ？ GPT and Autoencoders Designing Machine Learning Pipelines (MLOps) and Their Testing Designing and Implementing Large-Scale, Robust ML Software Part 4: Ethical Aspects of Data Management and ML System Development Ethics in Data Acquisition and Management Ethics in Machine Learning Systems Integrating ML Systems in Ecosystems Summary and Where to Go Next Index Other Books You May Enjoy