微软技术助力机器学习：项目选型与工具指南

Machine

Learning

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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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微软技术助力机器学习：项目选型与工具指南

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