Showing 2833–2844 of 4482 results
Mining Data from Images
OpenCV is a widely-used data science and machine learning software library. This course will teach you the basics of Image Processing and Analysis using OpenCV with Python, including feature detection, image classification, and object detection.
Mining Data from Text
This course discusses text and document feature vectors that can be passed into machine learning models, topic modeling using Latent Semantic Analysis, Latent Dirichlet Allocation, Non-negative Matrix Factorization, and keyword extraction using RAKE.
Mitigate Threats Using Microsoft 365 Defender
In this course you’re going to cover the skills measured in the Mitigate Threats using 365 Defender objective in the exam guide.
Mitigate Threats Using Microsoft Defender
This course will teach you how to enable, configure, and deploy Microsoft Defender in alignment with the Microsoft Security Operations Analyst (SC-200) exam.
Mitigate Threats Using Microsoft Sentinel
This course will teach you how to enable, configure, and monitor both cloud and non-cloud platforms using Microsoft Sentinel needed for the Microsoft Security Operations Analyst (SC-200).
MLOps Concepts
Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
Mobile First Responsive Web Design
This course is an introduction to concepts behind mobile-first responsive web design.
Mocking Node.js with Sinon 8
Creating unit tests is a skill, just like any other part of software development. This course will teach you how to effectively isolate your unit tests with spies, stubs, mocks, and fakes.
Mocking with Moq 4 and NUnit
Writing unit tests is hard when dependencies between classes make it tough to separate what's being tested from the rest of the system. Moq is a mocking library for .NET that makes it easy to create mock objects and make writing unit tests easier.
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 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.