Automated Machine Learning with AutoGluon in Python

Machine learning doesn't have to require hundreds of lines of complex code and manual hyperparameter tuning. With Automated Machine Learning (AutoML), you can train robust, state-of-the-art models with just a few lines of Python. This text-based course guides you from the fundamental concepts of automated machine learning to building and evaluating your own predictive pipelines. You will discover how to automate feature engineering, model selection, and ensembling using AutoGluon, while adhering to modern Python practices. What you'll learn: Understand the core concepts of Automated Machine Learning (AutoML) and model training workflows; Prepare and clean tabular data using modern Python libraries for optimal model performance; Train high-performance regression and classification models automatically with AutoGluon; Evaluate model predictions using modern validation techniques and performance metrics; Optimize model ensembles and explore deep learning capabilities within AutoML pipelines; Apply basic MLOps concepts to save, load, and prepare automated models for production. The course begins with essential machine learning terminology and setup before moving step-by-step through practical data preparation, model training, and evaluation. By the end of this reading journey, you will confidently apply AutoML to solve real-world predictive challenges. This course is designed for beginners, data enthusiasts, and software developers who want to leverage machine learning without needing a deep background in advanced statistics. No prior machine learning experience is required, though a basic familiarity with Python is helpful. Start reading today to simplify your machine learning workflow with AutoGluon.