Through detailed exploration and practical examples, the book delves into core aspects such as data preprocessing, model selection, hyperparameter tuning, and deployment strategies, shedding light on the seamless integration of these processes. Readers will gain insights into overcoming challenges and will be introduced to state-of-the-art methodologies and future trends. Emphasizing both theoretical understanding and practical applications, "Essential AutoML" equips readers with the knowledge to effectively implement AutoML solutions, enhancing productivity and innovation across diverse fields. This book is an indispensable resource for data scientists, IT professionals, and anyone keen on exploring the potential of machine learning automation.
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