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

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Free Download Exploring Data Science with .NET using Polyglot Notebooks & ML.NET
Released 10/2024
With Matt Eland
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 2h 1m 11s | Size: 220 MB

Learn to conduct data analytics and data science experiments using Polyglot Notebooks. Explore core concepts, language support, data ingestion, exploratory analysis, and more.
Course details
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.
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Exploring Data Science with .NET using Polyglot Notebooks & ML.NET
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 53m | 222 MB
Instructor: Matt Eland​

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.

Learning objectives

• Analyze and apply the core concepts of Polyglot Notebooks, including its relationship with Jupyter Notebooks and support for various programming languages
• Construct exploratory data analysis pipelines by ingesting data, performing descriptive statistics, and creating visualizations using appropriate libraries.
• Evaluate and implement basic machine learning models for classification and regression tasks using ML.NET's AutoML capabilities, including training, testing, and assessing model performance

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[0dayddl]

Exploring Data Science with .NET using Polyglot Notebooks & ML.NET
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 53m | 222 MB
Instructor: Matt Eland​

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.

Learning objectives

• Analyze and apply the core concepts of Polyglot Notebooks, including its relationship with Jupyter Notebooks and support for various programming languages
• Construct exploratory data analysis pipelines by ingesting data, performing descriptive statistics, and creating visualizations using appropriate libraries.
• Evaluate and implement basic machine learning models for classification and regression tasks using ML.NET's AutoML capabilities, including training, testing, and assessing model performance

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