Course Outline
Introduction
Installation and Configuration of Machine Learning for the .NET Development Platform (ML.NET)
- Setting up ML.NET tools and libraries
- Supported operating systems and hardware components for ML.NET
Overview of ML.NET Features and Architecture
- The ML.NET Application Programming Interface (ML.NET API)
- ML.NET machine learning algorithms and task capabilities
- Probabilistic programming utilizing Infer.NET
- Determining the appropriate ML.NET dependencies
Overview of the ML.NET Model Builder
- Integrating Model Builder with Visual Studio
- Leveraging automated machine learning (AutoML) via Model Builder
Overview of the ML.NET Command-Line Interface (CLI)
- Generating machine learning models through automation
- Machine learning tasks supported by the ML.NET CLI
Acquiring and Loading Data from Resources for Machine Learning
- Using the ML.NET API for data processing
- Creating and defining data model classes
- Annotating ML.NET data models
- Methods for loading data into the ML.NET framework
Preparing and Adding Data to the ML.NET Framework
- Filtering data models using ML.NET filter operations
- Working with ML.NET DataOperationsCatalog and IDataView
- Normalization techniques for ML.NET data pre-processing
- Data conversion processes in ML.NET
- Handling categorical data for ML.NET model generation
Implementing ML.NET Machine Learning Algorithms and Tasks
- Binary and multi-class classification in ML.NET
- Regression techniques in ML.NET
- Grouping data instances using Clustering in ML.NET
- The Anomaly Detection machine learning task
- Ranking, Recommendation, and Forecasting capabilities in ML.NET
- Selecting the appropriate ML.NET algorithm for specific data sets and functions
- Data transformation techniques in ML.NET
- Algorithms for enhancing the accuracy of ML.NET models
Training Machine Learning Models in ML.NET
- Building an ML.NET model from scratch
- ML.NET methods for training machine learning models
- Splitting data sets for ML.NET training and testing phases
- Managing different data attributes and scenarios in ML.NET
- Caching data sets to optimize ML.NET model training
Evaluating Machine Learning Models in ML.NET
- Extracting parameters for model retraining or inspection
- Collecting and recording ML.NET model metrics
- Analyzing the overall performance of a machine learning model
Inspecting Intermediate Data During ML.NET Model Training Steps
Utilizing Permutation Feature Importance (PFI) for Interpretation of Model Predictions
Saving and Loading Trained ML.NET Models
- ITTransformer and DataViewScheme concepts in ML.NET
- Loading data stored locally or remotely
- Managing machine learning model pipelines within ML.NET
Leveraging a Trained ML.NET Model for Data Analysis and Predictions
- Configuring the data pipeline for model predictions
- Performing single and multiple predictions in ML.NET
Optimizing and Re-training ML.NET Machine Learning Models
- Re-trainable algorithms within ML.NET
- Loading, extracting, and re-training models
- Comparing parameters of re-trained models with previous ML.NET models
Integrating ML.NET Models with The Cloud
- Deploying ML.NET models using Azure functions and web APIs
Troubleshooting
Summary and Conclusion
Requirements
- Familiarity with machine learning algorithms and libraries
- Proficiency in the C# programming language
- Experience working with .NET development platforms
- Fundamental understanding of data science tools
- Experience with basic machine learning applications
Target Audience
- Data Scientists
- Machine Learning Developers
Testimonials (2)
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