Note: This course is designed with ML.Net 1.5.0-preview2
Machine Learning is learning from experience and making predictions based on its experience.
In Machine Learning, we need to create a pipeline, and pass training data based on that Machine will learn how to react on data.
ML.NET gives you the ability to add machine learning to .NET applications.
We are going to use C# throughout this series, but F# also supported by ML.Net.
ML.Net officially publicly announced in Build 2019.
It is a free, open-source, and cross-platform.
It is available on both the dotnet core as well as the dotnet framework.
The course outline includes:
Introduction to Machine Learning. And understood how it’s different from Deep Learning and Artificial Intelligence.
Learn what is ML.Net and understood the structure of ML.Net SDK.
Create a first model for Regression. And perform a prediction on it.
Evaluate model and cross-validate with data.
Load data from various sources like file, database, and binary.
Filter out data from the data view.
Export created the model and load saved model for performing further operations.
Learn about binary classification and use it for creating a model with different trainers.
Perform sentimental analysis on text data to determine user’s intention is positive or negative.
Use the Multiclass classification for prediction.
Use the TensorFlow model for computer vision to determine which object represent by images.
Then we will see examples of using other trainers like Anomaly Detection, Ranking, Forecasting, Clustering, and Recommendation.
Perform Transformation on data related to Text, Conversion, Categorical, TimeSeries, etc.
Then see how we can perform AutoML using ModelBuilder UI and CLI.
Learn what is ONNX, and how we can create and use ONNX models.
Then see how we can use models to perform predictions from ASP.Net Core.
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