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Predictive Modeling with AutoML in Python: An Overview

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Automated Machine Learning (AutoML) is a powerful technique for building predictive models without requiring explicit model selection, hyperparameter tuning, or feature engineering. In this description, we'll explore how to perform predictive modeling using AutoML in Python. We'll cover an introduction to AutoML, installing necessary libraries, using AutoML models to solve regression and classification problems, and comparing the performances of these models.

AutoML is a popular approach for data scientists and machine learning engineers who want to build accurate models quickly and efficiently. With AutoML, extensive experimentation to select the best model and parameters for a specific problem is automated, saving valuable time and resources.

As we dive deeper into this topic, consider reading more about AutoML technologies and their underlying concepts:

[Automated Machine Learning: An Overview](https://www.ibm.com/topics/automated-...)
[Auto-sklearn: An Automated Machine Learning System for Python](https://auto-sklearn.org/)


Additional Resources:
[TensorFlow's AutoML documentation](https://www.tensorflow.org/tutorials/...)
[AutoSklearn User Guide](https://automl.github.io/auto-sklearn...)

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