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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
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
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
The trainer very easily explained difficult and advanced topics.
Leszek K
All like it
蒙 李
Communication with lecturers
文欣 张
like it all
lisa xie
I genuinely liked excercises
- L M ERICSSON LIMITED
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
The Jupyter notebook form, in which the training material is available
- L M ERICSSON LIMITED
There were many exercises and interesting topics.
- L M ERICSSON LIMITED
Some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)
- L M ERICSSON LIMITED
It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.
Attila Nagy - L M ERICSSON LIMITED
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
Kamila Begej - GE Medical Systems Polska Sp. Zoo
I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Issues discussed, exercises carried out (examples), atmosphere of training, contact with the trainer, location.
- Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu
I like that it focuses more on the how-to of the different text summarization methods
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Ewa has a passion for the subject and a huge wealth of knowledge. She impressed all of us with her knowledge and kept us all focused through the day.
Rock Solid Knowledge Ltd
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
So much breadth and topics covered. I felt it was a huge subject to try and cover in 3 days - the trainer did what they could to cover everything almost exactly on time!
Rock Solid Knowledge Ltd
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
convolution filter
Francesco Ferrara - Inpeco SpA
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
I like that it focuses more on the how-to of the different text summarization methods
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