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Download Torrent "PacktPub | Hands-On Machine Learning for .NET Developers [FCO]"

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Title:PacktPub | Hands-On Machine Learning for .NET Developers [FCO]
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By: Karl Tillström
Released: June 2020
Course Source:

Use machine learning today without a machine learning background

Video Details

ISBN 9781800205024
Course Length 2 hours 47 minutes


• Quickly implement machine learning algorithms directly within your current cross-platform .Net applications, such as ASP.Net Web.APIs, desktop applications, and Dotnet core console apps
• Use the advances in machine learning with models customized to your needs
• Automatically evaluate different machine learning models fast using AutoML, Model Builder, and CLI tools
• Improve and retrain your models for better performance and accuracy
• Basic overview of machine learning through a hands-on approach
• Use different machine learning algorithms to solve problems such as sentiment prediction, document classification, image recognition, product recommender systems, price predictions, and Bitcoin price forecasting
• Data loading and preparation for model training
• Leverage state of the art TensorFlow and ONNX models directly in .NET


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

• 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


Karl Tillström

Karl Tillström 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:

Category:Tutorials > Tutorials
Lang:English  English
Total Size:1.66 GB
Info Hash:97794eab6014b24456e47f04ca46bdf4292d8dcc
Added By:Prom3th3uS
Date Added:28-06-2020 23:50:16



File List: 
 File Size
0. Websites you may like/0. (1Hack.Us) Premium Tutorials-Guides-Articles _ Community based Forum.url377.00 B
0. Websites you may like/1. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url286.00 B
0. Websites you may like/3. ( Download Cracked Developers Applications For Free.url239.00 B
0. Websites you may like/How you can help our Group!.txt208.00 B
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/01.The Course Overview.mp49 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/02.Demo of the Application and How to Apply Machine Learning.mp412 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/03.Installing the ML.NET Model Builder.mp47 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/04.Automatically Generate a Model with the ML.NET Model Builder.mp48 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/05.Using the Final Model in the Desktop Application.mp416 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/06.Generating the Model Using the ML.NET CLI Tool.mp47 MB
2.Determining Aggression in User Comments/07.Demo of the Web API and the Wikipedia Aggression Dataset.mp47 MB
2.Determining Aggression in User Comments/08.Digging into the Code Learn What a Training Pipeline Is.mp415 MB
2.Determining Aggression in User Comments/09.Implementing a Pipeline for the Aggression Scorer.mp418 MB
2.Determining Aggression in User Comments/10.Using the Custom Model in the Web API.mp422 MB
3.Evaluating, Improving, and Retraining Your Model/11.Evaluating Your Model.mp417 MB
3.Evaluating, Improving, and Retraining Your Model/12.Splitting the Data into Training and Test Sets.mp48 MB
3.Evaluating, Improving, and Retraining Your Model/13.Retraining the Model with More Data.mp419 MB
3.Evaluating, Improving, and Retraining Your Model/14.Evaluating with Cross-Validation.mp416 MB
4.Classifying News into Subjects/15.Multiclass Classification and the UCI News Dataset.mp412 MB
4.Classifying News into Subjects/16.Using AutoML to Find a Suitable Model.mp412 MB
4.Classifying News into Subjects/17.Building the Pipeline and Evaluating the Performance.mp413 MB
4.Classifying News into Subjects/18.Explore the Effect of Imbalanced Data on the Metrics.mp415 MB
5.Building a Recommender System/19.The Restaurant Recommender.mp48 MB
5.Building a Recommender System/20.Building the Restaurant Recommendation Model.mp411 MB
5.Building a Recommender System/21.Exploring Hyper Parameters to Improve the Accuracy.mp442 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/22.Image Classification and Our Dataset.mp46 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/23.Deep Learning and Transferring Learnings from TensorFlow.mp415 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/24.Training the Custom Image Classification Model.mp421 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/25.Using the Trained Model in the Desktop Application.mp410 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/26.Speeding Up Model Training Using the GPU.mp424 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/27.What ONNX Is.mp46 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/28.The FER+ ONNX Model.mp417 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/29.Creating Our ONNX Pipeline.mp413 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/30.Detecting Emotions in Images and Webcam.mp422 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/31.Saving a ML.NET Model in ONNX Format.mp410 MB
Exercise Files/9781800205024_Code/SourceCode.zip1.23 GB


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