AutoML refers to the process of automating machine learning tasks such as model selection, hyperparameter tuning, and feature engineering. The goal of AutoML is to simplify the machine learning process and make it accessible to non-experts, while still maintaining high performance.

AutoML can be used to solve a variety of problems, including classification, regression, and time-series forecasting. It can also be applied to a range of industries, such as finance, healthcare, and retail.

The use of AutoML can greatly increase the efficiency and accuracy of machine learning tasks, making it a valuable tool for data scientists and non-experts alike.

R/Python packages provide a variety of tools for automating the machine learning process and are a great starting point for anyone looking to simplify and streamline their work. These tools use techniques such as reinforcement learning, evolutionary algorithms, and gradient descent to automate the machine learning process and optimize model performance.

Here are some popular R packages for Automated Machine Learning (AutoML):

1. H2O.ai: An open-source platform for machine learning that provides an easy-to-use interface for automating many aspects of the machine learning process, including feature engineering and model selection.

2. mlr: A package that provides a unified interface for machine learning with a focus on automating model selection and hyperparameter tuning.

3. caret: A package that provides a unified interface for training and testing machine learning models, as well as tools for feature engineering and model selection.

4. AutoML: A package that provides a set of functions for automating the machine learning process, including feature engineering, model selection, and hyperparameter tuning.

5. rpart: A package that provides a flexible and powerful interface for building decision tree models, including support for automating the model selection process.

Here are some popular Python packages for Automated Machine Learning (AutoML):

1. Auto-Sklearn: An open-source library for automating the machine learning process, including model selection and hyperparameter tuning.

2. TPOT (Tree-based Pipeline Optimization Tool): An open-source library for automating the machine learning process using genetic programming, including feature engineering and model selection.

3. scikit-learn: A popular machine learning library that provides a simple interface for automating many aspects of the machine learning process, including model selection and hyperparameter tuning.

4. PyCaret : PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and can help to make you more productive.