Showing 13105–13116 of 18767 results
Model Building and Evaluation for Data Scientists
Building and evaluating machine learning (ML) models is daunting, but correctly engineered models can provide millions of dollars in value. In this course, you'll learn to build and evaluate these tools, leveraging existing data science knowledge.
Model Building and Validation
Understand the processes of building, validating, and optimizing predictive models for data-driven decision-making.
Model data in Power BI
Connect Power BI to multiple data sources to create reports. Define the relationship between your data sources.
Model data with Power BI
Learn what a Power BI semantic model is, which data loading approach to use, and how to build out your semantic model to get the insights you need.
Model Deployment and Maintenance for Data Scientists
The machine learning pipeline doesn’t end at just building the model. This course will teach you how to deploy your machine learning models as application programming interface (API) endpoints, and the maintenance required to support the model.
Model Evaluation and Refinement Made Easy
Master model evaluation techniques to refine and improve your machine learning models. Learn to use metrics like accuracy, precision, recall, and F1-score to assess performance and tune models for optimal results.
Model Evaluation and Selection Using scikit-learn
Review the techniques and metrics used to evaluate how well your machine learning model performs. You will also learn methods to select the best machine learning model from a set of models that you've built.
Model Training: Best Practices for Data Practitioners
Pre-trained models are used everywhere right now to add AI functionalities to products. But have you wondered how they are trained? This course will teach you from start to finish the process of going from an idea and a dataset to a trained model.
Model Validation in Python
Learn the basics of model validation, validation techniques, and begin creating validated and high performing models.
Modeling Data in Power BI
Learn how to work with data that comes from different sources and is structured in different ways by using Power BI transformations to combine, reshape, cleanse, and enhance that data to create a model that supports reporting and analytics.
Modeling Requirements
This course gives you the skills to visually model requirements to get consensus, buy-in, and to help validate and verify requirements for lasting and successful solutions.
Modeling Streaming Data for Processing with Apache Beam
The Apache Beam unified model allows us to process batch as well as streaming data using the same API. Several execution backends such as Google Cloud Dataflow, Apache Spark, and Apache Flink are compatible with Beam.