Data Science
Showing 1153–1164 of 1255 results
Train and evaluate clustering models
Clustering is a type of machine learning that is used to group similar items into clusters.
Train and evaluate deep learning models
Deep learning is an advanced form of machine learning that emulates the way the human brain learns through networks of connected neurons.
Train and evaluate regression models
Regression is a commonly used kind of machine learning for predicting numeric values.
Train and manage a machine learning model with Azure Machine Learning
To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and manage a machine learning model.
Train and track machine learning models with MLflow in Microsoft Fabric
In Microsoft Fabric, data scientists can train models in notebooks, track their work in experiments, and manage their models with MLflow.
Train compute-intensive models with Azure Machine Learning
Train compute-intensive models with GPU compute in Azure Machine Learning. By monitoring workloads, you can find the optimal compute configuration. Distributed training allows you to train on multiple nodes to speed up training time.
Train compute-intensive models with Azure Machine Learning
Large-scale machine-learning and deep-learning models require ample compute power. Learn when to choose GPU compute, and how different frameworks help you to make optimal use of GPU compute during preprocessing, model training, and deployment.
Train deep learning models in Azure Databricks
Deep learning uses neural networks to train highly effective machine learning models for complex forecasting, computer vision, natural language processing, and other AI workloads.
Train models in Azure Machine Learning with the CLI (v2)
The Azure Machine Learning CLI (v2) is an Azure CLI extension that you can use to train and deploy machine learning models. Learn how to use the CLI (v2) to create Azure Machine Learning workspace assets to use for model training and deployment.
Train models with scripts in Azure Machine Learning
To prepare your machine learning workloads for production, you'll work with scripts. Learn how to train models with scripts in Azure Machine Learning.
Transferring Data with ETL
This course covers creating and orchestrating ETL pipelines using the industry’s best practices and tools: Python, SQL, Apache Spark, and Apache Airflow.
Transform data by implementing pivot, unpivot, rollup, and cube
Learn how to transform data using Transact-SQL.