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[FreeCoursesOnline.Me] PacktPub - Hands-On Machine Learning For DotNET Developers
TORRENT SUMMARY
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Use machine learning today without a machine learning background
Video Details
ISBN 9781800205024
Course Length 2 hours 47 minutes
Learn
• 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
About
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
Features:
• 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
Author
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: https://www.machinelearningfordevelopers.com.
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FILE LIST
Filename
Size
0. Websites you may like/How you can help our Group!.txt
208 B
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/01.The Course Overview.mp4
9.4 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/02.Demo of the Application and How to Apply Machine Learning.mp4
12.3 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/03.Installing the ML.NET Model Builder.mp4
6.7 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/04.Automatically Generate a Model with the ML.NET Model Builder.mp4
7.5 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/05.Using the Final Model in the Desktop Application.mp4
16 MB
1.Finding the Best Price on Laptops Using Price Prediction (Regression)/06.Generating the Model Using the ML.NET CLI Tool.mp4
7 MB
2.Determining Aggression in User Comments/07.Demo of the Web API and the Wikipedia Aggression Dataset.mp4
6.9 MB
2.Determining Aggression in User Comments/08.Digging into the Code Learn What a Training Pipeline Is.mp4
14.7 MB
2.Determining Aggression in User Comments/09.Implementing a Pipeline for the Aggression Scorer.mp4
17.9 MB
2.Determining Aggression in User Comments/10.Using the Custom Model in the Web API.mp4
21.6 MB
3.Evaluating, Improving, and Retraining Your Model/11.Evaluating Your Model.mp4
17.5 MB
3.Evaluating, Improving, and Retraining Your Model/12.Splitting the Data into Training and Test Sets.mp4
7.5 MB
3.Evaluating, Improving, and Retraining Your Model/13.Retraining the Model with More Data.mp4
18.5 MB
3.Evaluating, Improving, and Retraining Your Model/14.Evaluating with Cross-Validation.mp4
16 MB
4.Classifying News into Subjects/15.Multiclass Classification and the UCI News Dataset.mp4
11.7 MB
4.Classifying News into Subjects/16.Using AutoML to Find a Suitable Model.mp4
12.3 MB
4.Classifying News into Subjects/17.Building the Pipeline and Evaluating the Performance.mp4
13 MB
4.Classifying News into Subjects/18.Explore the Effect of Imbalanced Data on the Metrics.mp4
15.4 MB
5.Building a Recommender System/19.The Restaurant Recommender.mp4
7.9 MB
5.Building a Recommender System/20.Building the Restaurant Recommendation Model.mp4
11.2 MB
5.Building a Recommender System/21.Exploring Hyper Parameters to Improve the Accuracy.mp4
42 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/22.Image Classification and Our Dataset.mp4
5.9 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/23.Deep Learning and Transferring Learnings from TensorFlow.mp4
15.3 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/24.Training the Custom Image Classification Model.mp4
21.4 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/25.Using the Trained Model in the Desktop Application.mp4
9.5 MB
6.Classifying Images Using TensorFlow 'Transfer Learning'/26.Speeding Up Model Training Using the GPU.mp4
24 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/27.What ONNX Is.mp4
6.2 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/28.The FER+ ONNX Model.mp4
16.8 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/29.Creating Our ONNX Pipeline.mp4
12.9 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/30.Detecting Emotions in Images and Webcam.mp4
21.6 MB
7.Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model/31.Saving a ML.NET Model in ONNX Format.mp4
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