Ultimate Machine Learning with ML.NET

“Empower Your .NET Journey with Machine Learning”

Key Features
● Step-by-step guidance to help you navigate through various machine learning tasks and techniques with ML.NET.
● Explore all aspects of ML.NET, from installation and configuration to model deployment.
● Engage in practical exercises and real-world projects to solidify your understanding.

Book Description
Dive into the world of machine learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET.The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it using essential tools like Model Builder and the command-line interface. Next, it dives into the heart of machine learning tasks using ML.NET, exploring classification, regression, and clustering with its versatile functionalities.

It will delve deep into the process of selecting and fine-tuning algorithms to achieve optimal performance and accuracy. You will gain valuable insights into inspecting and interpreting ML.NET models, ensuring they meet your expectations and deliver reliable results. It will teach you efficient methods for saving, loading, and sharing your models across projects, facilitating seamless collaboration and reuse.

The final section of the book covers advanced techniques for optimizing model accuracy and refining performance. You will be able to deploy your ML.NET models using Azure Functions and Web API, empowering you to integrate machine learning solutions seamlessly into real-world applications.

What you will learn
● Understand the basics of ML.NET and its capabilities in the machine learning landscape.
● Gain practical experience with the ML.NET Model Builder and command-line interface (CLI) to efficiently create models.
● Understand how to choose the most suitable algorithms and fine-tune them for optimal performance within ML.NET.

Who is this book for?
This book is tailored for professionals and enthusiasts such as software developers, data scientists, and machine learning engineers who want to build and deploy machine learning models within the .NET ecosystem. IT professionals and technical leads overseeing machine learning projects in a .NET environment will also find this book valuable. Readers should have basic programming knowledge and a foundational understanding of machine learning concepts.

1. Introduction to ML.NET
2. Installing and Configuring ML.NET
3. ML.NET Model Builder and CLI
4. Collecting and Preparing Data for ML.NET
5. Machine Learning Tasks in ML.NET
6. Choosing and Tuning Machine Learning Algorithms in ML.NET
7. Inspecting and Interpreting ML.NET Models
8. Saving and Loading Models in ML.Net
9. Optimizing ML.NET Models for Accuracy
10. Deploying ML.NET Models with Azure Functions and Web API
Index

Kalicharan Mahasivabhattu is a seasoned industry expert with over 21 years of experience working with leading global organizations primarily in oil and gas and healthcare. As a Certified Artificial Intelligence and Machine Learning Specialist, he has earned the moniker Serial Innovator for his ground-breaking ideas in deep learning, augmented reality, chatbots, and computer vision, all of which have garnered support from the innovation council. Kali's dedication to advancing the field extends to his engaging podcast, Talking AWS for Data Science, where he shares insights and discusses cutting-edge developments.

Deepti Bandi is an experienced professional with over 17 years of exposure in the field of leveraging advanced analytics to drive business growth and decision- making. With a dual master’s degree in Computational Mechanics and Structural Engineering, she possesses a unique combination of technical expertise and business acumen. As a Data Science Team Manager for a healthcare company, she identifies potential use cases for advanced analytics projects and leads the development and implementation of advanced analytical solutions that address stakeholders' needs and drive business growth. In addition to her professional work, Deepti is also an accomplished writer, having published four white papers so far, including two at the prestigious Offshore Technology Conference. Deepti is an avid traveler who loves exploring new places and cultures. With 5 years of international work experience, she brings a global perspective to her work and is adept at working across diverse teams and cultures.

ABOUT TECHNICAL REVIEWER

Shanthababu has over 21 years of extensive experience in Information Technology (IT). He specializes in Data Engineering and Analytics solutions, Data Science, Machine Learning and Gen AI development and implementation. He leverages agile methodologies to build complex data models for Business Intelligence (BI) and data science products for various customers located across the UK and US regions.

He holds a bachelor’s degree in Electronics and Communication Engineering, followed by three Master's degrees - MTech, MBA, and M.S. - from a prestigious university in India. Additionally, he completed a Postgraduate Program in Artificial Intelligence and Machine Learning from the University of Texas and a Postgraduate Certification in Data Science from the Indian Institute of Technology, Guwahati.

He is working as the Director of Data and AI in a Product, Service and Consulting organization in London. Shanthababu is not just a leader but a passionate advocate for team building and nurturing. He assists global customers in achieving business value by leveraging reliable and scalable data engineering approaches and adopting AI and GenAI technologies. His dedication to leading data-driven transformations and building and nurturing teams of experts is a testament to his commitment to his work and his team.