Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft's powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. * 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you * Explore what's known about how humans learn and how intelligent software is built * Discover which problems machine learning can address * Understand the machine learning pipeline: the steps leading to a deliverable model * Use AutoML to automatically select the best pipeline for any problem and dataset * Master ML.NET, implement its pipeline, and apply its tasks and algorithms * Explore the mathematical foundations of machine learning * Make predictions, improve decision-making, and apply probabilistic methods * Group data via classification and clustering * Learn the fundamentals of deep learning, including neural network design * Leverage AI cloud services to build better real-world solutions faster About This Book * For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills * Includes examples of machine learning coding scenarios built using the ML.NET library

The expert guide to creating production machine learning solutions with ML.NET! ML.NET brings the power of machine learning to all .NET developers- and Programming ML.NET helps you apply it in real production solutions. Modeled on Dino Esposito's best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsoft's team used to build ML.NET itself. After a foundational overview of ML.NET's libraries, the authors illuminate mini-frameworks ("ML Tasks") for regression, classification, ranking, anomaly detection, and more. For each ML Task, they offer insights for overcoming common real-world challenges. Finally, going far beyond shallow learning, the authors thoroughly introduce ML.NET neural networking. They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network- showing how to leverage popular Python tools within .NET. 14-time Microsoft MVP Dino Esposito and son Francesco Esposito show how to: Build smarter machine learning solutions that are closer to your user's needs See how ML.NET instantiates the classic ML pipeline, and simplifies common scenarios such as sentiment analysis, fraud detection, and price prediction Implement data processing and training, and "productionize" machine learning-based software solutions Move from basic prediction to more complex tasks, including categorization, anomaly detection, recommendations, and image classification Perform both binary and multiclass classification Use clustering and unsupervised learning to organize data into homogeneous groups Spot outliers to detect suspicious behavior, fraud, failing equipment, or other issues Make the most of ML.NET's powerful, flexible forecasting capabilities Implement the related functions of ranking, recommendation, and collaborative filtering Quickly build image classification solutions with ML.NET transfer learning Move to deep learning when standard algorithms and shallow learning aren't enough "Buy" neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow