Author Description

Discover the powerful world of Automated Machine Learning (AutoML) with this comprehensive guide! Key Features: - Learn how to automate the entire machine learning pipeline using Python - Explore popular AutoML frameworks and libraries such as Auto-Sklearn and H2O.ai - Implement AutoML techniques for regression, classification, time series analysis, NLP, computer vision, and recommendation systems - Understand how to handle imbalanced data, interpret and visualize models, and optimize performance - Gain insights into utilizing AutoML in distributed systems, cloud platforms, and IoT landscapes - Discover real-world industry use cases in healthcare, finance, retail, manufacturing, and energy Book Description: Automated Machine Learning (AutoML) revolutionizes the field of data science by automating the time-consuming and complex tasks associated with building machine learning models. This book serves as a comprehensive guide to AutoML, providing a step-by-step approach to implementing automated machine learning pipelines using Python and popular frameworks like Auto-Sklearn and H2O.ai. Begin with an introduction to AutoML, exploring the key concepts and terminologies, and understanding the machine learning pipeline. Discover the need for AutoML and how it overcomes the limitations of traditional machine learning methods. Dive into the core components of AutoML, including automated data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and meta-learning. Explore popular AutoML frameworks and libraries such as Auto-Sklearn, H2O.ai, TPOT, DataRobot, MLJar, and Google AutoML. Learn how to automate data preprocessing tasks such as handling missing values, encoding categorical variables, and feature scaling. Discover techniques for automated feature engineering, algorithm selection, hyperparameter tuning, model evaluation and selection, and ensemble methods. Apply AutoML t