Machine Learning for C# Developers Made Easy
Machine Learning for C# Developers Made Easy
Build smart applications with ML.NET
About the Book
Create production quality machine learning models in C# without leaving the .NET ecosystem.
Machine Learning for C# Developers teaches you how to build powerful machine learning (ML) models using your C# and .NET skills—no Python required! You’ll learn how to use the innovative ML.NET framework to build a virtual assistant that can recognize objects, classify software errors, estimate salaries from a job description, and more.
In Machine Learning for C# Developers you’ll learn:
- Machine learning fundamentals
- Supervised, unsupervised, and reinforced model training
- Build and train models with C# code
- Automating the machine learning process
Machine learning is a powerful tool for forecasting trends, modeling customer behaviors, and identifying other important patterns in your data that will help you make more informed decisions. Recent advances in deep learning make it possible to build powerful ML-driven tools that can do everything from personalized product recommendations to image recognition, to text and code generation. For data scientists and developers, powerful frameworks like ML.NET and automated machine learning (AutoML) systems can greatly enhance your productivity in building, training, and deploying even the most advanced ML models.
About the Author
Table of Contents
- 1. ML.NET: C# developer’s gateway to machine learning
- 1.1 What is ML.NET?
- 1.2 Why is machine learning in demand?
- 1.3 What makes ML.NET powerful?
- 1.4 Where can ML.NET be used?
- 1.5 Organizations that use ML.NET
- 1.6 How can ML.NET build a useful model?
- 1.7 Installing ML.NET
- Summary
- 2. Machine learning fundamentals
- 2.1 Types of machine learning
- 2.2 Supervised machine learning
- 2.3 Unsupervised machine learning
- 2.4 Reinforcement machine learning
- 2.4.2 Exploitation and exploration
- 2.4.3 Reinforcement learning applications
- 2.5 Using ML.NET for supervised learning
- 2.6 Using ML.NET for unsupervised learning
- Summary
- 3. Selecting a problem for ML to solve
- 3.1 Selecting an ML task
- 3.2 Supervised training tasks available in ML.NET
- 3.3 Unsupervised training tasks available in ML.NET
- 3.4 Evaluating the training results
- Summary
- 4. Training a virtual assistant
- 4.1 Setting up our project
- 4.2 Classifying inputs
- 4.3 Estimating numeric values
- 4.4 Adding recommendation engine
- 4.5 Teaching our assistant to perform forecasting
- Summary
- 5. Detecting patterns with unsupervised learning
- 5.1 Detecting patterns in the data
- 5.2 Detecting anomalies
- 5.3 Removing noise in the data
- 5.4 Project: building our own clustering models
- Summary
- 6. Building an intelligent chatbot
- 6.1 Introduction to natural language processing
- 6.2 Applying sentence similarity
- 6.3 Applying text classification
- 6.4 Teaching the chatbot to answer questions
- 6.5 Named entity recognition technique
- 6.6 Project: refining chatbot capabilities
- Summary
- 7. Computer vision and making sense of images
- 7.1 Training an image classification model
- 7.2 Training an object detection model
- 7.3 Integrating with TensorFlow for image classification
- 7.4 How computer vision works
- 7.5 Project: building an intelligent shopping system
- Summary
- 1. ML.NET: C# developer’s gateway to machine learning
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