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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
Testimonials
The knowledge of the trainer was very high and the material was well prepared and organised.
Otilia - Gareth Morgan, TCMT
Machine Learning with Python – 2 Days Course
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
Jenna - Gareth Morgan, TCMT
Machine Learning with Python – 2 Days Course
Bardzo merytoryczne szkolenie, bardzo duża wiedza prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
Humor prowadzącego.
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
Wiedza i umiejetnosc jej przekazania
Danuta Haber, Orange Szkolenia Sp. z o.o.
Feature Engineering for Machine Learning Course
The details and the presentation style.
Cristian Mititean - Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
Interactive, a lot of exercises
Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
The Exercises
Khaled Altawallbeh - Edina Kiss, Accenture Industrial SS
Azure Machine Learning (AML) Course
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Margaret Elizabeth Webb, Department of Jobs, Regions, and Precincts
Artificial Neural Networks, Machine Learning, Deep Thinking Course
Going through the notebooks, becoming more familiar with Qiskit and the various ways to do things.
Bank of Canada
Practical Quantum Computing Course
Very very competent trainer who know how to adapt to his audience, and to solve problems Interactive presentation
OLEA MEDICAL
MLflow Course
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
MLflow Course
The way of transferring knowledge and the knowledge of the trainer.
Jakub Rękas - Sebastian Pawłowski, Bitcomp Sp. z o.o.
Machine Learning on iOS Course
The explaination
Wei Yang Teo - Ministry of Defence, Singapore
Machine Learning with Python – 4 Days Course
The trainer took the time to answer all our questions.
Ministry of Defence, Singapore
Machine Learning with Python – 4 Days Course
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Kubeflow Course
Working with real industry-leading ML tools, real datasets and being able to consult with a very experienced data scientist.
Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Applied AI from Scratch in Python Course
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
Machine Learning Concepts for Entrepreneurs and Managers Course
Convolution filter
Francesco Ferrara - Inpeco SpA
Introduction to Machine Learning Course
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Kubeflow Course
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Applied AI from Scratch in Python Course
I like that it focuses more on the how-to of the different text summarization methods
Text Summarization with Python Course
There were many exercises and interesting topics.
- L M ERICSSON LIMITED
Machine Learning Course
The Jupyter notebook form, in which the training material is available
- L M ERICSSON LIMITED
Machine Learning Course
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
Machine Learning Course
The trainer was so knowledgeable and included areas I was interested in
Mohamed Salama
Data Mining & Machine Learning with R Course
It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.
Jonathan Blease
Artificial Neural Networks, Machine Learning, Deep Thinking Course
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