Hands-On Machine Learning with ML.NET

Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#

Jarred Capellman

• Publisher: Packt Publishing
• Year: 2020
• Print ISBN: 9781789801781
• eBook ISBN: 9781789804294
• Edition: 1
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core

Key Features

• Get well-versed with the ML.NET framework and its components and APIs using practical examples
• Learn how to build, train, and evaluate popular machine learning algorithms with ML.NET offerings
• Extend your existing machine learning models by integrating with TensorFlow and other libraries

Book Description

Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help.NET enthusiasts in working with ML. With this book, you'll explore how to build ML.NET applications with the various ML models available using C# code.

The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You'll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You'll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within.NET applications. You'll also learn to integrate TensorFlow in ML.NET applications. Later you'll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR.

By the end of this book, you'll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET.

What you will learn

• Understand the framework, components, and APIs of ML.NET using C#
• Develop regression models using ML.NET for employee attrition and file classification
• Evaluate classification models for sentiment prediction of restaurant reviews
• Work with clustering models for file type classifications
• Use anomaly detection to find anomalies in both network traffic and login history
• Work with ASP.NET Core Blazor to create an ML.NET enabled web application
• Integrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detection

Who this book is for

If you are a.NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or.NET is mandatory to grasp the concepts covered in this book effectively.

Section 1: Fundamentals of Machine Learning and ML.NET

This section gives an overview of this book's audience and a short introduction to machine learning and the importance of learning how to utilize machine learning. In addition, this section introduces the reader to ML.NET. It also talks about the tools and framework needed to build the applications and gives a step-by-step explanation of how to work with ML.NET.

This section comprises the following chapters:

• Chapter 1, Getting Started with Machine Learning and ML.NET
• Chapter 2, Setting Up the ML.NET Environment

Getting Started with Machine Learning and ML.NET

By opening this book, you are taking the first step in disrupting your own knowledge by approaching solutions to complex problems with machine learning. You will be achieving this with the use of Microsoft's ML.NET framework. Having spent several years applying machine learning to cybersecurity, I'm confident that the knowledge you garner from this book will not only open career opportunities to you but also open up your thought processes and change the way you approach problems. No longer will you even approach a complex problem without thinking about how machine learning could possibly solve it.

Over the course of this book, you will learn about the following:

• How and when to use five different algorithms that ML.NET provides
• Real-world end-to-end examples demonstrating ML.NET algorithms
• Best practices when training your models, building your training sets, and feature engineering
• Using pre-trained models in both TensorFlow and ONNX formats

This book does assume that you have a reasonably solid understanding of C#. If you have other experience with a strongly typed object-oriented programming language such as C++ or Java, the syntax and design patterns are similar enough to not hinder your ability to follow the book. However, if this is your first deep dive into a strongly typed language such as C#, I strongly suggest picking up Learn C# in 7 Days, by Gaurav Aroraa, published by Packt Publishing, to get a quick foundation. In addition, no prior machine learning experience is required or expected, although a cursory understanding will accelerate your learning.

In this chapter, we will cover the following:

• The importance of learning about machine learning today
• The model-building process
• Exploring types of learning
• Exploring various machine learning algorithms
• Introduction to ML.NET

By the end of the chapter, you should have a fundamental understanding of what it takes to build a model from start to finish, providing the basis for the remainder of the book.

The importance of learning about machine learning today

In recent years, machine learning and artificial intelligence have become an integral part of many of our lives in use cases as diverse as finding cancer cells in an MRI and facial and object recognition during a professional basketball game. Over the course of just the four years between 2013 and 2017, machine learning patents alone grew 34%, while spending is estimated to grow to $57.6B by 2021 (https://www.forbes.com/sites/louiscolumbus/2018/02/18/roundup-of-machine-learning-forecasts-and-market-estimates-2018/#794d6f6c2225).

Despite its status as a growing technology, the term machine learning was coined back in 1959 by Arthur Samuel—so what caused the 60-year gap before its adoption? Perhaps the two most significant factors were the availability of technology able to process model predictions fast enough, and the amount of data being captured every minute digitally. According to DOMO Inc, a study in 2017 concluded that 2.5 quintillion bytes were generated daily and that at that time, 90% of the world's data was created between 2015 and 2017 (https://www.domo.com/learn/data-never-sleeps-5?aid=ogsm072517_1&sf100871281=1). By 2025, it is estimated that 463 exabytes of data are going to be created daily (https://www.visualcapitalist.com/how-much-data-is-generated-each-day/), much of which will come from cars, videos, pictures, IoT devices, emails, and even devices that have not made the transition to the smart movement yet.

The amount that data has grown in the last decade has led to questions about how a business or corporation can use such data for better sales forecasting, anticipating a customer's needs, or detecting malicious bytes in a file. Traditional statistical approaches could potentially require exponentially more staff to keep up with current demands, let alone scale with the data captured. Take, for instance, Google Maps. With Google's acquisition of Waze in 2013, users of Google Maps have been provided with extremely accurate routing suggestions based on the anonymized GPS data of its users. With this model, the more data points (in this case GPS data from smartphones), the better predictions Google can make for your travel. As we will discuss later in this chapter, quality datasets are a critical component of machine learning, especially in the case of Google Maps, where, without a proper dataset, the user experience would be subpar.

In addition, the speed of computer hardware, specifically specialized hardware tailored for machine learning, has also played a role. The use of Application-Specific Integrated Circuits (ASICs) has grown exponentially. One of the most popular ASICs on the market is the Google Tensor Processing Unit (TPU). Originally released in 2016, it has since gone through two iterations and provides cloud-based acceleration for machine learning tasks on Google Cloud Platform. Other cloud platforms, such as Amazon's AWS and Microsoft's Azure, also provide FPGAs.

Additionally, Graphics Processing Units (GPUs) from both AMD and NVIDIA are accelerating both cloud-based and local workloads, with ROCm Platform and CUDA-accelerated libraries respectively. In addition to accelerated workloads, typical professional GPUs offered by AMD and NVIDIA provide a much higher density of processors than the traditional CPU-only approach. For instance, the AMD Radeon Instinct MI60 provides 4,096 stream processors. While not a full-fledged x86 core, it is not a one-to-one comparison, and the peak performance of double-precision floating-point tasks is rated at 7.373 TFLOPs compared to the 2.3 TFLOPs in AMD's extremely powerful EPYC 7742 server CPU. From a cost and scalability perspective, utilizing GPUs in even a workstation configuration would provide an exponential reduction in training time if the algorithms were accelerated to take advantage of the more specialized cores offered by AMD and NVIDIA. Fortunately, ML.NET provides GPU acceleration with little additional effort.

From a software engineering career perspective, with this growth and demand far outpacing the supply, there has never been a better time to develop machine learning skills as a software engineer. Furthermore, software engineers also possess skills that traditional data scientists do not have – for instance, being able to automate tasks such as the model building process rather than relying on manual scripts. Another example of where a software engineer can provide more value is by adding both unit tests and efficacy tests as part of the full pipeline when training a model. In a large production application, having these automated tests is critical to avoid production issues.

Finally, in 2018, for the first time ever, data was considered more valuable than oil. As industries continue to adopt the use of data gathering and existing industries take advantage of the data they have, machine learning will be intertwined with the data. Machine learning to data is what refining plants are to oil.

The model building process

Before diving into ML.NET, an understanding of core machine learning concepts is required. These concepts will help create a foundation for you to build on as we start building models and learning the various algorithms ML.NET provides over the course of this book. At a high level, producing a model is a complex process; however, it can be broken down into six main steps:

Over the next few sections, we will go through each of these steps in detail to provide you with a clear understanding of how to perform each step and how each step relates to the overall machine learning process as a whole.

Defining your problem statement

Effectively, what problem are you attempting to solve? Being specific at this point is crucial as a less concise problem can lead to considerable re-work. For example, take the following problem statement: Predicting the outcome of an election. My first question upon hearing that problem statement would be, at what level? County, state, or national? Each level more than likely requires considerably more features and data to properly predict than the last. A better problem statement, especially early on in your machine learning journey, would be for a specific position at a county level, such as Predicting the 2020 John Doe County Mayor. With this more direct problem statement, your features and dataset are much more focused and more than likely attainable. Even with more experience in machine learning, proper scoping of your problem statement is critical. The five Ws of Who, What, When, Where, and Why should be followed to keep your statement concise.

Defining your features

The second step in machine learning is defining your features. Think of features as components or attributes of the problem you wish to solve. In machine learning – specifically, when creating a new model – features are one of the biggest impacts on your model's performance. Properly thinking through your problem statement will promote an initial set of features that will drive differentiation between your dataset and model results. Going back to the Mayor example in the preceding section, what features would you consider data points for the citizen? Perhaps start by looking at the Mayor's competition and where he/she sits on issues in ways that differ from other candidates. These values could be turned into features and then made into a poll for citizens of John Doe County to answer. Using these data points would create a solid first pass at features. One aspect here that is also found in model building is running several iterations of feature engineering and model training, especially as your dataset grows. After model evaluation, feature importance is used to determine what features are actually driving your predictions. Occasionally, you will find that gut-instinct features can actually be inconsequential after a few iterations of model training and feature engineering.

In Chapter 11, Training and Building Production Models, we will deep dive into best practices when defining features and common approaches to complex problems to obtain a solid first pass at feature engineering.

Obtaining a dataset

As you can imagine, one of the most important aspects of the model building process is obtaining a high-quality dataset. A dataset is used to train the model on what the output should be in the case of the aforementioned case of supervised learning. In the case of unsupervised learning, labeling is required for the dataset. A common misconception when creating a dataset is that bigger is better. This is far from the truth in a lot of cases. Continuing the preceding example, what if all of the poll results answered the same way for every single question? At that point, your dataset is composed of all the same data points and your model will not be able to properly predict any of the other candidates. This outcome is called overfitting. A diverse but representative dataset is required for machine learning algorithms to properly build a production-ready model.

In Chapter 11, Training and Building Production Models, we will deep dive into the methodology of obtaining quality datasets, looking at helpful resources, ways to manage your datasets, and transforming data, commonly referred to as data wrangling.

Feature extraction and pipeline

Once your ...

Table of contents

• Title Page
• Copyright and Credits
• Dedication
• About Packt
• Contributors
• Preface
• Section 1: Fundamentals of Machine Learning and ML.NET
• Getting Started with Machine Learning and ML.NET
• Setting Up the ML.NET Environment
• Section 2: ML.NET Models
• Regression Model
• Classification Model
• Clustering Model
• Anomaly Detection Model
• Matrix Factorization Model
• Section 3: Real-World Integrations with ML.NET
• Using ML.NET with .NET Core and Forecasting
• Using ML.NET with ASP.NET Core
• Using ML.NET with UWP
• Section 4: Extending ML.NET
• Training and Building Production Models
• Using TensorFlow with ML.NET
• Using ONNX with ML.NET
• Other Books You May Enjoy