Hands-On Machine Learning for .NET Developers

Hands-On Machine Learning for .NET Developers, available at $54.99, has an average rating of 4, with 31 lectures, 7 quizzes, based on 145 reviews, and has 1107 subscribers.

You will learn about Quickly implement machine learning algorithms directly within your current cross-platform .Net applications, such as ASP .Net Web .APIs, desktop applications, and Dotnet core console apps Use the advances in machine learning with models customized to your needs Automatically evaluate different machine learning models fast using AutoML, Model Builder, and CLI tools Improve and retrain your models for better performance and accuracy Basic overview of machine learning through a hands-on approach Use different machine learning algorithms to solve problems such as sentiment prediction, document classification, image recognition, product recommender systems, price predictions, and Bitcoin price forecasting Data loading and preparation for model training Leverage state of the art TensorFlow and ONNX models directly in .NET This course is ideal for individuals who are This course is for .NET developers who want to implement custom machine learning models using ML .NET and ML developers who are looking for effective tools to implement various machine learning algorithms. This course is also suitable for data scientists who want to implement machine learning in .Net. It is particularly useful for This course is for .NET developers who want to implement custom machine learning models using ML .NET and ML developers who are looking for effective tools to implement various machine learning algorithms. This course is also suitable for data scientists who want to implement machine learning in .Net.

Enroll now: Hands-On Machine Learning for .NET Developers

Summary

Title: Hands-On Machine Learning for .NET Developers

Price: $54.99

Average Rating: 4

Number of Lectures: 31

Number of Quizzes: 7

Number of Published Lectures: 31

Number of Published Quizzes: 7

Number of Curriculum Items: 38

Number of Published Curriculum Objects: 38

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Quickly implement machine learning algorithms directly within your current cross-platform .Net applications, such as ASP .Net Web .APIs, desktop applications, and Dotnet core console apps

Use the advances in machine learning with models customized to your needs

Automatically evaluate different machine learning models fast using AutoML, Model Builder, and CLI tools

Improve and retrain your models for better performance and accuracy

Basic overview of machine learning through a hands-on approach

Use different machine learning algorithms to solve problems such as sentiment prediction, document classification, image recognition, product recommender systems, price predictions, and Bitcoin price forecasting

Data loading and preparation for model training

Leverage state of the art TensorFlow and ONNX models directly in .NET

Who Should Attend

This course is for .NET developers who want to implement custom machine learning models using ML .NET and ML developers who are looking for effective tools to implement various machine learning algorithms. This course is also suitable for data scientists who want to implement machine learning in .Net.

Target Audiences

This course is for .NET developers who want to implement custom machine learning models using ML .NET and ML developers who are looking for effective tools to implement various machine learning algorithms. This course is also suitable for data scientists who want to implement machine learning in .Net.

ML.NET enables developers utilize their .NET skills to easily integrate machine learning into virtually any .NET application. This course will teach you how to implement machine learning and build models using Microsoft’s new Machine Learning library, ML.NET. You will learn how to leverage the library effectively to build and integrate machine learning into your .NET applications.

By taking this course, you will learn how to implement various machine learning tasks and algorithms using the ML.NET library, and use the Model Builder and CLI to build custom models using AutoML.

You will load and prepare data to train and evaluate a model; make predictions with a trained model; and, crucially, retrain it. You will cover image classification, sentiment analysis, recommendation engines, and more! You’ll also work through techniques to improve model performance and accuracy, and extend ML.NET by leveraging pre-trained TensorFlow models using transfer learning in your ML.NET application and some advanced techniques.

By the end of the course, even if you previously lacked existing machine learning knowledge, you will be confident enough to perform machine learning tasks and build custom ML models using the ML.NET library.

About the Author

Karl Tillstr?m has been passionate about making computers do amazing things ever since childhood and is strongly driven by the magic possibilities you can create using programming. This makes advances in machine learning and AI his holy grail; since he took his first class in artificial neural networks in 2007, he has experimented with machine learning by building all sorts of things, ranging from Bitcoin price prediction to self-learning Gomoku playing AI.

Karl is a software engineer and systems architect with over 15 years’ professional experience in .Net, building a wide variety of systems ranging from airline mobile check-ins to online payment systems.

Driven by his passion, he took a Master’s degree in Computer Science and Engineering at the Chalmers University of Technology, a top university in Sweden.

Course Curriculum

Chapter 1: Finding the Best Price on Laptops Using Price Prediction (Regression)

Lecture 1: The Course Overview

Lecture 2: Demo of the Application and How to Apply Machine Learning

Lecture 3: Installing the ML.NET Model Builder

Lecture 4: Automatically Generate a Model with the ML.NET Model Builder

Lecture 5: Using the Final Model in the Desktop Application

Lecture 6: Generating the Model Using the ML.NET CLI Tool

Chapter 2: Determining Aggression in User Comments

Lecture 1: Demo of the Web API and the Wikipedia Aggression Dataset

Lecture 2: Digging into the Code Learn What a Training Pipeline Is

Lecture 3: Implementing a Pipeline for the Aggression Scorer

Lecture 4: Using the Custom Model in the Web API

Chapter 3: Evaluating, Improving, and Retraining Your Model

Lecture 1: Evaluating Your Model

Lecture 2: Splitting the Data into Training and Test Sets

Lecture 3: Retraining the Model with More Data

Lecture 4: Evaluating with Cross-Validation

Chapter 4: Classifying News into Subjects

Lecture 1: Multiclass Classification and the UCI News Dataset

Lecture 2: Using AutoML to Find a Suitable Model

Lecture 3: Building the Pipeline and Evaluating the Performance

Lecture 4: Explore the Effect of Imbalanced Data on the Metrics

Chapter 5: Building a Recommender System

Lecture 1: The Restaurant Recommender

Lecture 2: Building the Restaurant Recommendation Model

Lecture 3: Exploring Hyper Parameters to Improve the Accuracy

Chapter 6: Classifying Images Using TensorFlow “Transfer Learning”

Lecture 1: Image Classification and Our Dataset

Lecture 2: Deep Learning and Transferring Learnings from TensorFlow

Lecture 3: Training the Custom Image Classification Model

Lecture 4: Using the Trained Model in the Desktop Application

Lecture 5: Speeding Up Model Training Using the GPU

Chapter 7: Detecting Facial Expressions in Your Webcam with a Pre-Trained ONNX Model

Lecture 1: What ONNX Is

Lecture 2: The FER+ ONNX Model

Lecture 3: Creating Our ONNX Pipeline

Lecture 4: Detecting Emotions in Images and Webcam

Lecture 5: Saving a ML.NET Model in ONNX Format

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 5 votes

2 stars: 5 votes

3 stars: 14 votes

4 stars: 62 votes

5 stars: 59 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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