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Natural Language Processing with ML.NET (Video)

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Description

  • Copyright 2024
  • Edition: 1st
  • Online Video
  • ISBN-10: 0-13-836120-7
  • ISBN-13: 978-0-13-836120-4

Over 3 Hours of Video Instruction

Learn how the ML.NET Framework can democratize the art of machine learning to integrate a pre-trained NLP model customized on your data into your .NET solution.

Overview:

Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language. Carlotta Castelluccio introduces the basic concepts of machine learning and then deep dives into one specific domain--natural language processing with ML.NET. She demonstrates how to fine-tune the hyper parameters of the model through the API, after having trained the model with Model Builder.

Some of the key points covered in this course are:

  • Setting up your machine to get started with ML.NET framework
  • Fine-tuning a pre-trained NLP model on your data with Model Builder and perform evaluation
  • Adding the NLP model to a console app and tune hyperparameters to get better performances
  • Deploying the model and consume it into a Razor web app

Skill Level:

  • Beginner to Intermediate

Learn How To:

  • Set up your machine to get started with the ML.NET framework (without any additional costs!)
  • Build an ML model with a low code approach and beginner knowledge of ML
  • Build and deploy a .NET app embedding a Natural Language Processing (NLP) model

Course requirement:

  • Some familiarity with Visual studio and/or .NET

Who Should Take This Course:

  • You are a software developer familiar with C# and .NET framework but not with languages traditionally used for data science (Python and R)
  • You are a beginner in machine learning, and you would like to get started with building and deploying models
  • You are a data scientist wishing to learn a new machine learning framework

More about Microsoft Press:

Microsoft Press creates IT books and references for all skill levels across the range of Microsoft technologies. https://www.microsoftpressstore.com/

More about Pearson Video Training:  

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video training at http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Downloads

Downloads

Download the code files from the author's GitHub:

Sample Content

Table of Contents

Introduction

Segment 1: Get started with ML.NET

1.1  What is Machine Learning

1.2  What is .NET ecosystem

1.3  What is ML.NET and how does it differ from other popular machine learning frameworks    

1.4  Exercise: Setting up your local machine to work with ML.NET framework 

1.5  Advanced exercise: Installing and configuring Visual Studio Code and Polyglot Notebooks    

1.6  For .NET developers: AI & ML in the .NET ecosystem

Segment 2: Classification in ML.NET

2.1  What is classification

2.2  Training and evaluating a classification model

2.3  Exercise: Training a classification model with Model Builder

2.4  Advanced Exercise: Training a classification model with AutoML and Polyglot Notebooks

Segment 3: Text classification & Sentence Similarity in ML.NET

3.1  What is Natural Language Processing (NLP) and how an NLP model works 

3.2  Text classification task within ML.NET framework 

3.3  Exercise: Fine-tuning a pre-trained NLP model on your data with Model Builder

3.4  Advanced concepts: Sentence similarity

3.5  Advanced Exercise: Sentence similarity with Model Builder

Segment 4: MLOps in ML.NET

4.1  What is MLOps

4.2  Deploying and consuming models into ML.NET framework  

4.3  Exercise: Deploying your .NET application on the Cloud

4.4  Advanced concepts: Azure Cloud and GitHub 

4.5  Advanced concepts: Responsible AI

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