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PacktPub Hands-On Machine Learning with ML.NET

Video Introducing this tutorial


Section 1: Fundamentals of Machine Learning and ML.NET:
Section 1: Fundamentals of Machine Learning and ML.NET

Getting Started with Machine Learning and ML.NET:
Getting Started with Machine Learning and ML.NET
The importance of learning about machine learning today
The model building process
Exploring types of learning
Exploring various machine learning algorithms
What is ML.NET?
Summary

Setting Up the ML.NET Environment:
Setting Up the ML.NET Environment
Setting up your development environment
Creating your first ML.NET application
Evaluating the model
Summary

Section 2: ML.NET Models:
Section 2: ML.NET Models

Regression Model:
Regression Model
Breaking down regression models
Creating the linear regression application
Creating the logistic regression application
Evaluating a regression model
Summary

Classification Model:
Classification Model
Breaking down classification models
Creating a binary classification application
Creating a multi-class classification application
Evaluating a classification model
Summary

Clustering Model:
Clustering Model
Breaking down the k-means algorithm
Creating the clustering application
Evaluating a k-means model
Summary

Anomaly Detection Model:
Anomaly Detection Model
Breaking down anomaly detection
Creating a time series application
Creating an anomaly detection application
Evaluating a randomized PCA model
Summary

Matrix Factorization Model:
Matrix Factorization Model
Breaking down matrix factorizations
Creating a matrix factorization application
Evaluating a matrix factorization model
Summary

Section 3: Real-World Integrations with ML.NET:
Section 3: Real-World Integrations with ML.NET

Using ML.NET with .NET Core and Forecasting:
Using ML.NET with .NET Core and Forecasting
Breaking down the .NET Core application architecture
Creating the stock price estimator application
Exploring additional production application enhancements
Summary

Using ML.NET with ASP.NET Core:
Using ML.NET with ASP.NET Core
Breaking down ASP.NET Core
Creating the file classification web application
Exploring additional ideas for improvements
Summary

Using ML.NET with UWP:
Using ML.NET with UWP
Breaking down the UWP architecture
Creating the web browser classification application
Additional ideas for improvements
Summary

Section 4: Extending ML.NET:
Section 4: Extending ML.NET

Training and Building Production Models:
Training and Building Production Models
Investigating feature engineering
Obtaining training and testing datasets
Creating your model-building pipeline
Summary

Using TensorFlow with ML.NET:
Using TensorFlow with ML.NET
Breaking down Google's Inception model
Creating the WPF image classification application
Additional ideas for improvements
Summary

Using ONNX with ML.NET:
Using ONNX with ML.NET
Breaking down ONNX and YOLO
Creating the ONNX object detection application
Exploring additional production application enhancements
Summary

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