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Hands-On Machine Learning for .NET Developers

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Karl Tillström

2:46:48

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  • 01.The Course Overview.mp4
    04:04
  • 02.Demo of the Application and How to Apply Machine Learning.mp4
    05:07
  • 03.Installing the ML.NET Model Builder.mp4
    03:04
  • 04.Automatically Generate a Model with the ML.NET Model Builder.mp4
    03:57
  • 05.Using the Final Model in the Desktop Application.mp4
    07:03
  • 06.Generating the Model Using the ML.NET CLI Tool.mp4
    03:16
  • 07.Demo of the Web API and the Wikipedia Aggression Dataset.mp4
    03:36
  • 08.Digging into the Code Learn What a Training Pipeline Is.mp4
    08:15
  • 09.Implementing a Pipeline for the Aggression Scorer.mp4
    06:40
  • 10.Using the Custom Model in the Web API.mp4
    07:32
  • 11.Evaluating Your Model.mp4
    07:28
  • 12.Splitting the Data into Training and Test Sets.mp4
    03:29
  • 13.Retraining the Model with More Data.mp4
    06:15
  • 14.Evaluating with Cross-Validation.mp4
    06:47
  • 15.Multiclass Classification and the UCI News Dataset.mp4
    05:36
  • 16.Using AutoML to Find a Suitable Model.mp4
    05:19
  • 17.Building the Pipeline and Evaluating the Performance.mp4
    04:27
  • 18.Explore the Effect of Imbalanced Data on the Metrics.mp4
    04:08
  • 19.The Restaurant Recommender.mp4
    03:57
  • 20.Building the Restaurant Recommendation Model.mp4
    04:03
  • 21.Exploring Hyper Parameters to Improve the Accuracy.mp4
    08:05
  • 22.Image Classification and Our Dataset.mp4
    03:02
  • 23.Deep Learning and Transferring Learnings from TensorFlow.mp4
    07:15
  • 24.Training the Custom Image Classification Model.mp4
    07:30
  • 25.Using the Trained Model in the Desktop Application.mp4
    03:13
  • 26.Speeding Up Model Training Using the GPU.mp4
    06:30
  • 27.What ONNX Is.mp4
    03:30
  • 28.The FER+ ONNX Model.mp4
    07:33
  • 29.Creating Our ONNX Pipeline.mp4
    04:31
  • 30.Detecting Emotions in Images and Webcam.mp4
    07:35
  • 31.Saving a ML.NET Model in ONNX Format.mp4
    04:01
  • Description


    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. All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Hands-On-Machine-Learning-for-.NET-Developers-V

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    Karl Tillström
    Karl Tillström
    Instructor's Courses
    Karl Tillstrm has been passionate about making computers do amazing things ever since childhood and is strongly driven by the magic possibilities you can create using programming. This makes advances in machine learning and AI his holy grail; since he took his first class in artificial neural networks in 2007, he has experimented with machine learning by building all sorts of things, ranging from Bitcoin price prediction to self-learning Gomoku playing AI. Karl is a software engineer and systems architect with over 15 years' professional experience in .Net, building a wide variety of systems ranging from airline mobile check-ins to online payment systems. Driven by his passion, he took a Master's degree in Computer Science and Engineering at the Chalmers University of Technology, a top university in Sweden. Follow him and learn more at: https://www.machinelearningfordevelopers.com.
    Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.
    • language english
    • Training sessions 31
    • duration 2:46:48
    • Release Date 2024/03/14