Course Outline

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

Requirements

  • Knowledge of machine learning algorithms and libraries
  • Strong command of C# programming language
  • Experience with .NET development platforms
  • Basic understanding of data science tools
  • Experience with basic machine learning applications

Audience

  • Data Scientists
  • Machine Learning Developers
 21 Hours

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