Automated Machine Learning

Table of Contents

• Automated Machine Learning
  • Table of Contents
  • Introduction
  • Installation
  • Conclusion

Introduction

Upload the dataset and get the best model in a few minutes. This is the power of Automated Machine Learning (AutoML). AutoML is the process of automating the end-to-end process of applying machine learning to real-world problems. Automated Machine Learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, we need to perform the following steps:

• Data Preprocessing
• Feature Engineering
• Feature Selection
• Model Selection
• Hyperparameter Tuning
• Model Evaluation

AutoML automates these steps, which makes it easier for non-experts to use machine learning models.

Installation

conda create -n automl python=3.8
conda activate automl
pip install -r requirements.txt
python app.py

Conclusion

Automated Machine Learning (AutoML) simplifies the process of building and deploying machine learning models, making it accessible to non-experts and allowing data scientists to focus on more complex tasks. By automating the steps of data preprocessing, feature engineering, feature selection, model selection, hyperparameter tuning, model evaluation, and model deployment, AutoML accelerates the development of machine learning solutions.

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