Use machine learning today without a machine learning background
About This Video
- Quickly get up and running using state-of-the-art machine learning algorithms in your .Net applications
- Implement machine learning algorithms using real-world data sets, without first learning math
- Leverage state-of-the-art (TensorFlow, ONNX) models, pre-trained by the tech giants, in your own .Net code
ML.NET enables developers utilize their .NET skills to easily integrate machine learning into virtually any .NET application. This course will teach you how to implement machine learning and build models using Microsoft's new Machine Learning library, ML.NET. You will learn how to leverage the library effectively to build and integrate machine learning into your .NET applications.
By taking this course, you will learn how to implement various machine learning tasks and algorithms using the ML.NET library, and use the Model Builder and CLI to build custom models using AutoML.
You will load and prepare data to train and evaluate a model; make predictions with a trained model; and, crucially, retrain it. You will cover image classification, sentiment analysis, recommendation engines, and more! You'll also work through techniques to improve model performance and accuracy, and extend ML.NET by leveraging pre-trained TensorFlow models using transfer learning in your ML.NET application and some advanced techniques.
By the end of the course, even if you previously lacked existing machine learning knowledge, you will be confident enough to perform machine learning tasks and build custom ML models using the ML.NET library.
This course is for .NET developers who want to implement custom machine learning models using ML.NET and ML developers who are looking for effective tools to implement various machine learning algorithms. This course is also suitable for data scientists who want to implement machine learning in .Net. Prior knowledge (and a basic understanding) of C# and .Net are necessary. However, prior machine learning knowledge or learning Python are not required.
Table of contents
- Chapter 1 : Finding the Best Price on Laptops Using Price Prediction (Regression)
- Chapter 2 : Determining Aggression in User Comments
- Chapter 3 : Evaluating, Improving, and Retraining Your Model
- Chapter 4 : Classifying News into Subjects
- Chapter 5 : Building a Recommender System
- Chapter 6 : Classifying Images Using TensorFlow "Transfer Learning"
- Chapter 7 : Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model
- Title: Hands-On Machine Learning for .NET Developers
- Release date: June 2020
- Publisher(s): Packt Publishing
- ISBN: 9781800205024
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