Course Outline
Introduction
Installing and Configuring Machine Learning for .NET Development Platform (ML.NET)
- Configuring ML.NET tools and libraries.
- Supported operating systems and hardware requirements for ML.NET.
Overview of ML.NET Features and Architecture
- Introduction to the ML.NET Application Programming Interface (API).
- Exploring ML.NET algorithms and supported tasks.
- Probabilistic programming using Infer.NET.
- Selecting appropriate ML.NET dependencies.
Overview of ML.NET Model Builder
- Integrating Model Builder with Visual Studio.
- Leveraging Automated Machine Learning (AutoML) via Model Builder.
Overview of ML.NET Command-Line Interface (CLI)
- 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.
- Creating and defining data model classes.
- Annotating data models within ML.NET.
- Common scenarios for loading data into the ML.NET framework.
Preparing and Injecting Data into the ML.NET Framework
- Filtering data models using ML.NET filter operations.
- Working with ML.NET DataOperationsCatalog and IDataView.
- Normalization techniques for data pre-processing in ML.NET.
- Data conversion strategies within ML.NET.
- Handling categorical data during model generation.
Implementing ML.NET Machine Learning Algorithms and Tasks
- Binary and multi-class classification techniques in ML.NET.
- Regression analysis in ML.NET.
- Clustering data instances in ML.NET.
- Anomaly detection as a machine learning task.
- Ranking, recommendation systems, and forecasting in ML.NET.
- Selecting the right algorithm for specific datasets and functions.
- Data transformation processes in ML.NET.
- Algorithms designed to enhance model accuracy.
Training Machine Learning Models in ML.NET
- Constructing an ML.NET model.
- Methods for training machine learning models within ML.NET.
- Partitioning datasets for training and testing purposes.
- Managing various data attributes and use cases in ML.NET.
- Caching datasets to facilitate model training.
Evaluating Machine Learning Models in ML.NET
- Extracting parameters for model inspection or retraining.
- Collecting and recording key performance metrics.
- Analyzing the overall performance of a machine learning model.
Inspecting Intermediate Data During Model Training
Utilizing Permutation Feature Importance (PFI) for Interpreting Model Predictions
Saving and Loading Trained ML.NET Models
- Understanding ITTransformer and DataViewScheme in ML.NET.
- Loading data stored both locally and remotely.
- Managing machine learning model pipelines in ML.NET.
Utilizing Trained ML.NET Models for Data Analysis and Predictions
- Configuring data pipelines for prediction outcomes.
- Executing single and multiple predictions in ML.NET.
Optimizing and Re-training an ML.NET Machine Learning Model
- Algorithms suitable for retraining in ML.NET.
- Loading, extracting, and retraining models.
- Comparing parameters of re-trained models against previous iterations.
Integrating ML.NET Models with Cloud Services
- Deploying ML.NET models using Azure Functions and Web APIs.
Troubleshooting
Summary and Conclusion
Requirements
- Foundational knowledge of machine learning algorithms and libraries.
- Proficiency in the C# programming language.
- Practical experience working with .NET development platforms.
- Basic familiarity with data science tools and environments.
- Prior exposure to fundamental 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
