In recent times, we hear these few things a lot:

最近，我们经常听到以下几件事：

First is —The COVID-19 Pandemic, of course! Second is — Quarantine and the Third is “Machine Learning’’. Yes, Machine Learning is so much in hype these days and no doubt that it is a technology of the future. Machine Learning, Artificial Intelligence, Data Science, etc are the pioneers of futuristic applications. Thus, in this article, we’ll talk about Machine Learning: What it is, How it works, at the beginner level!

首先是-COVID-19大流行，当然！ 第二是“隔离”，第三是“机器学习”。 是的，如今机器学习大肆宣传，毫无疑问，它是未来的技术。 机器学习，人工智能，数据科学等是未来应用程序的先驱。 因此，在本文中，我们将讨论机器学习：它是什么，它是如何工作的，在初学者级别！

So let’s start.

因此，让我们开始吧。

So first, let’s look into What is Machine Learning?

首先，让我们看看什么是机器学习？

Machine Learning is a subset of Artificial Intelligence and is defined as,

机器学习是人工智能的子集，定义为：

“The study of computer algorithms that improve automatically through experience”

“ 通过经验自动改进的计算机算法的研究 ”

Before we move ahead, let’s see how Tom Mitchell defines Machine Learning:

在继续进行之前，让我们看看Tom Mitchell如何定义机器学习：

“A well-posed learning problem is defined as, A computer program is said to learn from experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E ”

“ 一个恰当的学习问题被定义为，一个计算机程序被认为可以从经验E中学习一些任务T和一些绩效指标P，如果它对P的绩效(由P衡量)随经验E的提高而有所改善，那么， ”

Ok, I know, both the definitions might sound a bit overwhelming at the moment. Thus let’s see in simple terms how it works so that you will be able to appreciate their definitions. So in simple terms, what is a Machine Learning Algorithm?

好的，我知道，这两个定义目前可能听起来有些不知所措。 因此，让我们简单地看一下它是如何工作的，以便您能够欣赏它们的定义。 简单来说，什么是机器学习算法？

It’s like a child’s brain. When the kid is young you show him/her an Apple and tell that this is Apple. If you repeat this a few times, it establishes a connection with the child’s brain in order to recognize an Apple.

就像孩子的大脑。 当孩子年幼时，您可以给他/她一个苹果，并告诉他这是苹果。 如果重复几次，它将与孩子的大脑建立联系以识别苹果。

Suppose, next time the kid see’s an Apple he might be able to recognize an apple using its features such as color, size, shape, category, etc.

假设，下次孩子看到一个苹果时，他也许可以使用其颜色，大小，形状，类别等功能识别一个苹果。

Now, when we talk about machine learning, replace the child’s brain by a machine learning model, and replace the apple with some data.

现在，当我们谈论机器学习时，用机器学习模型替换孩子的大脑，并用一些数据替换苹果。

Thus, this is Machine Learning at a very basic level and this is how it works. Now, once you read the above definitions again, this time you would surely be able to understand and appreciate them.

因此，这是非常基础的机器学习，这就是它的工作方式。 现在，一旦您再次阅读以上定义，您肯定可以理解和欣赏它们。

Now that we know that what is Machine Learning and how it works, let’s look at the steps in Machine Learning:

现在我们知道什么是机器学习及其工作原理，下面让我们看一下机器学习中的步骤：

• Data Collection数据采集
• Data Preparation资料准备
• Choosing a Model选择模型
• Training a Model训练模型
• Evaluating a Model评估模型
• Parameter tuning参数调整
• Implementation实作

Thus, for a machine learning model, Data is an important aspect. From data collection to preparation for choosing and training the model makes machine learning algorithms.

因此，对于机器学习模型而言， 数据是重要的方面。 从数据收集到准备选择和训练模型，机器学习算法都可以使用。

Thus, let’s talk about the types of machine learning algorithms. At the most basic level, there are three types of learning:

因此，让我们讨论一下机器学习算法的类型。 在最基本的层次上，有三种学习类型：

1)监督学习 (1) Supervised Learning)

Supervised learning as the name indicates the presence of a supervisor as a teacher. Basically supervised learning is a learning in which we teach or train the machine using data that is well labeled which means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples (data) so that the supervised learning algorithm analyses the training data (set of training examples) and produces a correct outcome from labeled data.

监督学习的名称表示主管是教师。 基本上，监督学习是一种学习，我们在学习中使用标记正确的数据来教学或训练机器，这意味着某些数据已被正确答案标记。 之后，机器会提供一组新的示例(数据)，以便监督学习算法分析训练数据(一组训练示例)并从标记的数据中产生正确的结果。

2)无监督学习 (2) Unsupervised Learning)

Unsupervised learning is the training of machines using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.

无监督学习是使用既未分类也未标记的信息来训练机器，并允许算法在没有指导的情况下对信息进行操作。 在这里，机器的任务是根据相似性，模式和差异对未分类的信息进行分组，而无需事先训练数据。

Unlike supervised learning, no teacher is provided, which means no training will be given to the machine. Therefore the machine is restricted to find the hidden structure in unlabeled data by ourselves.

与监督学习不同，它不提供任何老师，这意味着不会对机器进行任何培训。 因此，机器只能自己寻找未标记数据中的隐藏结构。

3)强化学习 (3) Reinforcement Learning)

Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.

强化学习是机器学习的一个领域。 它是关于采取适当的措施以在特定情况下最大化回报。 它被各种软件和机器所采用，以找到在特定情况下应采取的最佳行为或路径。

Reinforcement learning differs from the supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself, whereas in reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task. In the absence of a training dataset, it is bound to learn from its experience.

强化学习与监督学习的不同之处在于，在监督学习中，训练数据具有答案键，因此可以使用正确的答案本身对模型进行训练，而在强化学习中，没有答案，而是由强化代理决定要做什么。执行给定的任务。 在没有训练数据集的情况下，它必然会从其经验中学习。

This is all about Machine Learning that you should know to get started with it.

这是关于机器学习的全部知识，您应该从入门开始就应该知道这一点。

What are your thoughts on machine learning and is it overhyped? Curious to know in the comments…

您对机器学习有什么看法，是否被夸大了？ 好奇地在评论中知道...