Course Outline
Introduction
Setup and Configuration of Machine Learning for the .NET Development Platform (ML.NET)
- Configuring ML.NET tools and libraries
- Supported operating systems and hardware components for ML.NET
Architectural and Feature Overview of ML.NET
- The ML.NET Application Programming Interface (ML.NET API)
- Available machine learning algorithms and tasks in ML.NET
- Probabilistic programming using Infer.NET
- Selecting the appropriate dependencies for ML.NET projects
Overview of ML.NET Model Builder
- Integrating Model Builder within Visual Studio
- Leveraging Automated Machine Learning (AutoML) via Model Builder
Overview of the ML.NET Command-Line Interface (CLI)
- Generating machine learning models automatically
- Machine learning tasks supported by the ML.NET CLI
Data Acquisition and Loading for Machine Learning
- Utilizing the ML.NET API for data processing
- Defining classes and creating data models
- Applying annotations to ML.NET data models
- Methods for loading data into the ML.NET framework
Data Preparation and Integration into the ML.NET Framework
- Filtering data models using ML.NET filter operations
- Interacting with ML.NET DataOperationsCatalog and IDataView
- Normalization techniques for ML.NET data preprocessing
- Data conversion processes within ML.NET
- Handling categorical data for model generation in ML.NET
Implementation of ML.NET Algorithms and Tasks
- Binary and Multi-class classification in ML.NET
- Regression analysis in ML.NET
- Clustering data instances using ML.NET
- Anomaly detection tasks
- Ranking, Recommendation, and Forecasting capabilities in ML.NET
- Selecting the optimal algorithm for specific datasets and functions
- Data transformation techniques in ML.NET
- Algorithms for enhancing ML.NET model accuracy
Training Machine Learning Models in ML.NET
- Constructing an ML.NET model
- Techniques for training machine learning models with ML.NET
- Splitting datasets for training and testing purposes
- Managing various data attributes and scenarios in ML.NET
- Caching datasets to optimize model training
Evaluating Machine Learning Models in ML.NET
- Extracting parameters for model inspection or retraining
- Gathering and logging model performance metrics
- Analyzing the overall performance of the machine learning model
Inspecting Intermediate Data During Model Training Steps in ML.NET
Interpreting Model Predictions using Permutation Feature Importance (PFI)
Saving and Loading Trained ML.NET Models
- Understanding ITTransformer and DataViewScheme in ML.NET
- Loading data from local and remote storage
- Managing machine learning model pipelines in ML.NET
Applying Trained ML.NET Models for Analysis and Prediction
- Configuring the data pipeline for predictive tasks
- Performing single and batch predictions in ML.NET
Optimization and Retesting of ML.NET Models
- Identifying re-trainable algorithms in ML.NET
- Processes for loading, extracting, and retraining models
- Comparing parameters between retrained and previous ML.NET models
Cloud Integration for ML.NET Models
- Deploying ML.NET models using Azure Functions and Web APIs
Troubleshooting Common Issues
Summary and Conclusion
Requirements
- Familiarity with machine learning algorithms and associated libraries
- Proficiency in the C# programming language
- Practical experience with .NET development platforms
- Foundational knowledge of data science tools
- Experience working with basic machine learning applications
Target Audience
- Data Scientists
- Machine Learning Engineers and Developers
Testimonials (2)
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