The book "Machine Learning in Python for Process and Equipment Condition Monitoring and Predictive Maintenance" is a comprehensive guide to using machine learning techniques in Python to monitor and predict the condition of processes and equipment in various industries. The book covers the entire spectrum of machine learning, from data collection and preprocessing to model selection and deployment, providing readers with a solid foundation in the field. The book begins by discussing the importance of condition monitoring and predictive maintenance in various industries, such as manufacturing, oil and gas, and power generation. It highlights the challenges faced by these industries, including equipment failure, downtime, and safety risks, and how machine learning can help address these challenges. The book then delves into the fundamentals of machine learning, explaining key concepts such as supervised and unsupervised learning, regression, classification, clustering, and neural networks. The next section of the book focuses on data preprocessing, which is a critical step in any machine learning application. It covers data cleaning, feature engineering, normalization, and transformation, emphasizing the need for high-quality data to achieve accurate predictions. The book also introduces several Python libraries commonly used in machine learning, such as NumPy, SciPy, and pandas.

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