The book provides a comprehensive understanding of Automated Machine Learning’s transformative potential across various industries, empowering users to seamlessly implement advanced Machine Learning solutions without needing extensive expertise. Automated Machine Learning (AutoML) is a process to automate the responsibilities of Machine Learning concepts for real-world problems. The AutoML process is comprised of all steps, beginning with a raw dataset and concluding with the construction of a Machine Learning model for deployment. The purpose of AutoML is to allow non-experts to work with Machine Learning models and techniques without requiring much knowledge in Machine Learning. This advancement enables data scientists to produce the easiest solutions and most accurate results within a short timeframe, allowing them to outperform normal Machine Learning models. Meta-learning, neural network architecture, and hyperparameter optimization, are applied based on AutoML.

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