微软技术助力机器学习：项目选型与工具指南

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"《机器学习与微软技术》一书，由Leila Etaati于2019年由Apress出版社发布，探讨了如何在商业软件开发领域广泛应用机器学习。本书的核心内容涵盖了机器学习的基本概念、方法和不同类型，以及其在微软产品中的实际应用，如Bing搜索引擎、Xbox和Kinect等。作者特别关注如何选择适合项目的架构和工具，使读者能够充分利用微软提供的强大机器学习工具，如SQL Server、Power BI和.NET等，以创建更智能的应用程序和报表。章节中深入剖析了机器学习项目的生命周期，从数据收集、预处理、模型选择到训练和部署，每个阶段都进行了详细指导。读者可以了解到微软技术生态系统中集成的机器学习工具的优势，以及如何有效地将这些工具应用于软件开发流程中，提升决策制定者对数据的洞察力，从而获取更为精准和深入的信息。此外，版权方面，所有内容受版权保护，未经许可，禁止任何形式的翻译、复制、重印、朗诵、广播、微缩胶片复制、物理或数字传输，以及任何形式的电子适应或类似技术的使用。书中可能会包含商标名称、标志和图像，尽管并非每次出现都标注商标符号。《机器学习与微软技术》是一本实用指南，对于希望在日常工作中利用微软平台进行机器学习实践的开发者和技术人员来说，提供了宝贵的参考和实战经验。通过阅读这本书，读者将能掌握如何在实际项目中构建和优化机器学习解决方案，提升软件产品的智能化水平。"

Academics and authors have proposed different definitions of machine learning.

For example, Sebastian Raschka defined machine learning as tools for making sense

of data, using algorithms [2]. He mentioned that we encounter a significant amount of

structured (numbers) and unstructured (image, voice text, and so forth) data. Gaining

insight from these data affects the decision-making process and helps managers to

achieve a better understanding of what happened, why it happened, what will happen in

future, and how to make it happen.

The concepts of machine learning are based on discovering common patterns from

current data sets. Historically, we created reports and software to understand what

happened in the past. Analyzing recent events and data always helps us to perform

further analysis, such as finding key performance indicators (KPIs), and so forth.

Figure 1-1. A Venn diagram showing the AI category and subcategories [1]

Chapter 1 IntroduCtIon toMaChIne LearnIng

Machine Learning Approaches

There are two main approaches to machine learning:

Supervised Learning

Unsupervised Learning

In the following paragraphs, a brief explanation of each is provided. In Chapter 5,

I will go into more detail about these and offer examples of both.

Supervised Learning

The primary goal of supervised learning is to learn how to predict a group or value from

past data. By another definition, supervised learning is the machine learning task of

inferring a function from labeled training data [2].

There are different approaches to supervised learning. One is to predict a value, for

example, predicting the number of subscribers to a video channel, which could range

from one to millions of individuals. Supervised learning makes it possibile to predict

how many people will subscribe to this channel. Another example is predicting sales

for a forthcoming year that could range from $1,000 to $200,000. By this approach, an

algorithm predicts the number of sales for the company. We call this method a regression

approach, in which the outcome is a continuous value.

The other approach to supervised learning involves predicting a group, for example,

predicting whether a customer will stay with a company or leave it. In this example, the

goal is to predict whether a current customer will remain with a group. Another case

could consider a company that provides different tiers of customers, such as gold, silver,

and bronze. In this case, the supervised learning approach might predict whether a

new customer will belong to the gold, silver, or bronze group. In this type of supervised

learning, the prediction column should be a discrete class label.

Unsupervised Learning

In supervised learning, we already have an idea of the answer before creating and

training the model. In unsupervised learning, we do not predict a column, and we do not

attach any label to the data. The main goal of unsupervised learning is to find the natural

data pattern, to explore the data and extract its meaningful information.

Chapter 1 IntroduCtIon toMaChIne LearnIng

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