Course Outline
Introduction
Installation and Configuration of Machine Learning for the .NET Development Platform (ML.NET)
- Setting up ML.NET tools and libraries.
- Compatible operating systems and hardware components for ML.NET.
ML.NET Features and Architecture Overview
- The ML.NET Application Programming Interface (API).
- Machine learning algorithms and tasks in ML.NET.
- Probabilistic programming using Infer.NET.
- Selecting the appropriate ML.NET dependencies.
ML.NET Model Builder Overview
- Integrating Model Builder with Visual Studio.
- Leveraging automated machine learning (AutoML) via Model Builder.
ML.NET Command-Line Interface (CLI) Overview
- Automated generation of machine learning models.
- Machine learning tasks supported by the ML.NET CLI.
Acquiring and Loading Data for Machine Learning
- Using the ML.NET API for data processing.
- Defining data model classes.
- Annotating ML.NET data models.
- Scenarios for loading data into the ML.NET framework.
Preparing and Ingesting Data into ML.NET
- Filtering data models using ML.NET operations.
- Working with ML.NET DataOperationsCatalog and IDataView.
- Normalization techniques for ML.NET data pre-processing.
- Data conversion within ML.NET.
- Handling categorical data for ML.NET model generation.
Implementing ML.NET Algorithms and Tasks
- Binary and multi-class classification in ML.NET.
- Regression in ML.NET.
- Grouping data instances via Clustering in ML.NET.
- Anomaly Detection tasks.
- Ranking, Recommendation, and Forecasting in ML.NET.
- Selecting the right ML.NET algorithm for specific datasets and functions.
- Data transformation in ML.NET.
- Algorithms for enhancing ML.NET model accuracy.
Training Models in ML.NET
- Constructing an ML.NET model.
- Methods for training machine learning models in ML.NET.
- Splitting datasets for training and testing in ML.NET.
- Managing different data attributes and scenarios in ML.NET.
- Caching datasets for ML.NET model training.
Evaluating ML.NET Models
- Extracting parameters for retraining or inspection.
- Recording ML.NET model metrics.
- Analyzing machine learning model performance.
Inspecting Intermediate Data During ML.NET Training Steps
Interpreting Model Predictions with Permutation Feature Importance (PFI)
Saving and Loading Trained ML.NET Models
- 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 Predictions
- Configuring data pipelines for model predictions.
- Single and multiple predictions in ML.NET.
Optimizing and Retraining ML.NET Models
- Re-trainable ML.NET algorithms.
- Loading, extracting, and retraining a model.
- Comparing retrained model parameters with the original ML.NET model.
Cloud Integration of ML.NET Models
- Deploying ML.NET models using Azure functions and web APIs.
Troubleshooting
Summary and Conclusion
Requirements
- Familiarity with machine learning algorithms and libraries.
- Proficiency in C# programming.
- Practical experience with .NET development platforms.
- Fundamental understanding of data science tools.
- Hands-on experience with basic machine learning applications.
Target Audience
- Data Scientists.
- Machine Learning Developers.
Testimonials (3)
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
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
