Prepare employees for a trade show with the help of this course.

Safeguard your intellectual property with trademark law. Learn to protect your logo, design, tagline, and brand identity with expert guidance.

This course covers the fundamentals of traditional compute options in the IBM Cloud® Platform.

Learn to train a hotdog image recognition model using Python. Understand the process of image classification and model training with real-world datasets for building accurate image classifiers.

Train a machine learning model in Visual Studio with ML.NET by using Model Builder, which uses sensor data to detect whether a manufacturing device is broken.

Machine learning involves using data to train a predictive model. Azure Databricks support multiple commonly used machine learning frameworks that you can use to train models.

Classification is a kind of machine learning used to categorize items into classes.

Clustering is a type of machine learning that is used to group similar items into clusters.

Deep learning is an advanced form of machine learning that emulates the way the human brain learns through networks of connected neurons.

Regression is a commonly used kind of machine learning for predicting numeric values.

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.

In Microsoft Fabric, data scientists can train models in notebooks, track their work in experiments, and manage their models with MLflow.