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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-mlnet-for-absolute-beginners/
课程评论：没有评论
课程名称：面向绝对初学者的机器学习与ML.Net 课程概述：本课程旨在介绍机器学习的基本概念，并使用ML.Net 1.5.0-preview2版本进行练习。机器学习是通过经验学习并根据经验进行预测的过程。在机器学习中，我们需要创建一个管道，并基于训练数据进行传递，从而使机器能够学习如何对数据做出反应。ML.NET使您能够将机器学习添加到.NET应用程序中，整个系列将使用C#进行编程，尽管F#也受ML.Net支持。ML.Net于2019年的Build大会上正式发布，是一个免费的、开源的、多平台的框架，适用于dotnet core和dotnet框架。 课程大纲包括： 1. 机器学习介绍，并了解其如何与深度学习和人工智能不同。 2. 学习ML.Net的基本概念，并理解ML.Net SDK的结构。 3. 创建第一个回归模型并进行预测。 4. 评估模型，并使用数据进行交叉验证。 5. 从各种来源（如文件、数据库和二进制文件）加载数据。 6. 从数据视图中过滤数据。 7. 导出创建的模型并加载保存的模型以进行后续操作。 8. 学习二元分类，并使用不同的训练器创建模型。 9. 对文本数据进行情感分析，以确定用户的意图是积极还是消极。 10. 使用多分类进行预测。 11. 运用TensorFlow模型进行计算机视觉，识别图像表示的物体。 12. 介绍其他训练器的例子，如异常检测、排序、预测、聚类和推荐。 13. 对与文本、转换、类别、时间序列等相关的数据进行转换。 14. 如何通过ModelBuilder UI和CLI执行AutoML。 15. 学习ONNX的概念，以及如何创建和使用ONNX模型。 16. 了解如何在ASP.Net Core中使用模型进行预测。 本课程为初学者提供了一个全面的机器学习基础，帮助学员逐步掌握使用ML.Net进行机器学习的技术和应用。
Note: This course is designed with ML.Net 1.5.0-preview2Machine Learning is learning from experience and making predictions based on its experience.In Machine Learning, we need to create a pipeline, and pass training data based on that Machine will learn how to react on data.ML.NET gives you the ability to add machine learning to.NET applications.We are going to use C# throughout this series, but F# also supported by ML.Net.ML.Net officially publicly announced in Build 2019.It is a free, open-source, and cross-platform.It is available on both the dotnet core as well as the dotnet framework.The course outline includes:Introduction to Machine Learning. And understood how it's different from Deep Learning and Artificial Intelligence.Learn what is ML.Net and understood the structure of ML.Net SDK.Create a first model for Regression. And perform a prediction on it.Evaluate model and cross-validate with data.Load data from various sources like file, database, and binary.Filter out data from the data view.Export created the model and load saved model for performing further operations.Learn about binary classification and use it for creating a model with different trainers.Perform sentimental analysis on text data to determine user's intention is positive or negative.Use the Multiclass classification for prediction.Use the TensorFlow model for computer vision to determine which object represent by images.Then we will see examples of using other trainers like Anomaly Detection, Ranking, Forecasting, Clustering, and Recommendation.Perform Transformation on data related to Text, Conversion, Categorical, TimeSeries, etc.Then see how we can perform AutoML using ModelBuilder UI and CLI.Learn what is ONNX, and how we can create and use ONNX models.Then see how we can use models to perform predictions from ASP.Net Core.
