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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 the Model Builder to 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 the classes of data models
- 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 for with 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 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
21 Hours
