Machine Learning and AI with ML.NET Training Course

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