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Exploring Data Science with .NET using Polyglot Notebooks & ML.NET

سرفصل های دوره

In this course, Matt Eland—an AI specialist, Microsoft MVP, and author—equips experienced .NET developers with the skills to conduct data analytics and data science experiments using Polyglot Notebooks. Dive into the core of Polyglot Notebooks, its relationship to Jupyter Notebooks, and language support for C#, F#, PowerShell, SQL, and Mermaid diagrams. Learn data ingestion, sharing between kernels, exploratory data analysis with descriptive statistics, and data visualization using libraries like Microsoft.Data.Analysis, ScottPlot, and Plotly.NET. Explore basic machine learning concepts, model training, train/test splits, evaluation, and beginner classification/regression experiments with ML.NET's AutoML capabilities. Plus, cover advanced Polyglot Notebooks integrations like Azure OpenAI, Semantic Kernel, Sequence Diagram Generation, and Azure AI Services.


01 - Introduction
  • 01 - Data science with .NET
  • 02 - What you should know

  • 02 - 1. Introducing Polyglot Notebooks
  • 01 - Notebooks and kernels
  • 02 - Installing Polyglot Notebooks
  • 03 - Creating your first Notebook
  • 04 - C# cells
  • 05 - Variable sharing between cells
  • 06 - Declaring classes and methods
  • 07 - F# cells
  • 08 - Sharing variables between kernels
  • 09 - Markdown cells
  • 10 - Mermaid diagrams
  • 11 - Importing NuGet packages

  • 03 - 2. Data Wrangling with DataFrames
  • 01 - Introducing DataFrames
  • 02 - Renaming and removing columns
  • 03 - Replacing missing values
  • 04 - Dropping missing values
  • 05 - Feature engineering
  • 06 - Merging DataFrames
  • 07 - Grouping data
  • 08 - Filtering data
  • 09 - Exporting DataFrames

  • 04 - 3. Data Analysis and Visualization with DataFrames and Plotly.NET
  • 01 - Describing DataFrames
  • 02 - Getting values from individual columns
  • 03 - Histograms
  • 04 - Box and violin plots
  • 05 - Scatter plots

  • 05 - 4. Machine Learning with ML.NET
  • 01 - Intro to machine learning, ML.NET, and AutoML
  • 02 - Loading data into traintest sets
  • 03 - Training classification models
  • 04 - Evaluating classification models
  • 05 - Training regression models
  • 06 - Evaluating regression models
  • 07 - Saving and loading models
  • 08 - Generating predictions from models
  • 09 - Additional ML.NET topics

  • 06 - 5. Deploying Polyglot Notebooks
  • 01 - Adopting Polyglot Notebooks
  • 02 - Getting into data science and AI as a developer

  • 07 - Conclusion
  • 01 - Next steps
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    تاریخ انتشار: ۸ اسفند ۱۴۰۳
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