This course will teach you how you can build and train regression, classification, and forecasting models using Databricks AutoML. AutoML automates data preparation and model training thus allowing you to build models with little to no code.
Databricks AutoML is an important step towards the democratization of machine learning. AutoML makes it easy for anyone to build and train robust models with little to no code. In this course, Automate Machine Learning Using Databricks AutoML, you will be introduced to the basic concepts of Databricks AutoML. First, you will see how AutoML automates every step of the machine learning process from data preparation, and data preprocessing to model training and evaluation. Next, you will first train regression and classification models using the AutoML user interface to configure your model training, and you can configure settings to impute missing values, choose model frameworks and evaluate models. Then, you will learn to use the AutoML Python API to train regression and classification models. The Python API offers simple regress() and classify() functions which you can configure using input parameters. Finally, you will work with time series datasets and train forecasting models using both the AutoML UI and the AutoML Python API. When you are finished with this course you will be able to confidently use AutoML to train regression, classification, and forecasting models and deploy them to production.
Databricks AutoML is an important step towards the democratization of machine learning. AutoML makes it easy for anyone to build and train robust models with little to no code. In this course, Automate Machine Learning Using Databricks AutoML, you will be introduced to the basic concepts of Databricks AutoML. First, you will see how AutoML automates every step of the machine learning process from data preparation, and data preprocessing to model training and evaluation. Next, you will first train regression and classification models using the AutoML user interface to configure your model training, and you can configure settings to impute missing values, choose model frameworks and evaluate models. Then, you will learn to use the AutoML Python API to train regression and classification models. The Python API offers simple regress() and classify() functions which you can configure using input parameters. Finally, you will work with time series datasets and train forecasting models using both the AutoML UI and the AutoML Python API. When you are finished with this course you will be able to confidently use AutoML to train regression, classification, and forecasting models and deploy them to production.
