Course Outline
Introduction.
Installing and Configuring Machine Learning for .NET Development Platform (ML.NET).
- Setting up ML.NET tools and libraries.
- Operating systems and hardware components supported by ML.NET.
Overview of ML.NET Features and Architecture.
- The ML.NET Application Programming Interface (ML.NET API).
- ML.NET machine learning algorithms and tasks.
- Probabilistic programming with Infer.NET.
- Deciding on the appropriate ML.NET dependencies.
Overview of ML.NET Model Builder.
- Integrating Model Builder into Visual Studio.
- Utilizing automated machine learning (AutoML) with Model Builder.
Overview of ML.NET Command-Line Interface (CLI).
- Automated machine learning model generation.
- Machine learning tasks supported by ML.NET CLI.
Acquiring and Loading Data from Resources for Machine Learning.
- Utilizing the ML.NET API for data processing.
- Creating and defining data model classes.
- Annotating ML.NET data models.
- Cases for loading data into the ML.NET framework.
Preparing and Adding Data Into the ML.NET Framework.
- Filtering data models using ML.NET filter operations.
- Working with ML.NET DataOperationsCatalog and IDataView.
- Normalization approaches for ML.NET data pre-processing.
- Data conversion in ML.NET.
- Working with categorical data for ML.NET model generation.
Implementing ML.NET Machine Learning Algorithms and Tasks.
- Binary and Multi-class ML.NET classifications.
- Regression in ML.NET.
- Grouping data instances with Clustering in ML.NET.
- Anomaly Detection machine learning task.
- Ranking, Recommendation, and Forecasting in ML.NET.
- Choosing the appropriate ML.NET algorithm for a data set and functions.
- Data transformation in ML.NET.
- Algorithms for improved accuracy of ML.NET models.
Training Machine Learning Models in ML.NET.
- Building an ML.NET model.
- ML.NET methods for training a machine learning model.
- Splitting data sets for ML.NET training and testing.
- Working with different data attributes and cases in ML.NET.
- Caching data sets for ML.NET model training.
Evaluating Machine Learning Models in ML.NET.
- Extracting parameters for model retraining or inspecting.
- Collecting and recording ML.NET model metrics.
- Analyzing the performance of a machine learning model.
Inspecting Intermediate Data During ML.NET Model Training Steps.
Utilizing Permutation Feature Importance (PFI) for Model Predictions Interpretation.
Saving and Loading Trained ML.NET Models.
- ITTransformer and DataViewScheme in ML.NET.
- Loading locally and remotely stored data.
- Working with machine learning model pipelines in ML.NET.
Utilizing a Trained ML.NET Model for Data Analyses and Predictions.
- Setting up the data pipeline for model predictions.
- Single and Multiple predictions in ML.NET.
Optimizing and Re-training an ML.NET Machine Learning Model.
- Re-trainable ML.NET algorithms.
- Loading, extracting and re-training a model.
- Comparing re-trained model parameters with previous ML.NET model.
Integrating ML.NET Models with The Cloud.
- Deploying an ML.NET model with Azure functions and web API.
Troubleshooting.
Summary and Conclusion.
Requirements
- Knowledge of machine learning algorithms and libraries.
- Strong command of the C# programming language.
- Experience with .NET development platforms.
- Basic understanding of data science tools.
- Experience with basic machine learning applications.
Audience
- Data Scientists.
- Machine Learning Developers.
Testimonials (1)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
