It is used in different fields such as in 1. Autonomous Vehicles 2. Speech Recognition. 3. Healthcare industry. 4. Machine Vision 5. Gaming industry. 6. Creating Chatbots. 7. Agriculture, Finance, Marketing, and Banking sectors. 8. Natural Language Processing (NLP). 9. Computer Vision. 10. Robotics And many more.

它用于不同的领域，例如1.自动驾驶汽车2.语音识别。 3.医疗保健行业。 4.机器视觉5.游戏产业。 6.创建聊天机器人。 7.农业，金融，市场和银行部门。 8.自然语言处理(NLP)。 9.计算机视觉。 10.机器人技术等等。

那么什么是AI？ (So What is AI?)

The name itself is not new, but the technology is far more modern. AI can be defined as building smart machines capable of doing work much like a human or beyond it. So devices require somewhat human intelligence.

这个名字本身并不是什么新鲜事物，但是技术却更加现代。 人工智能可以定义为构建能够像人类一样工作或超越人类工作的智能机器。 因此，设备需要一定程度的人类智能。

AI is mainly divided into three types based on its categories: 1. Weak /Narrow /Artificial Narrow Intelligence (ANI). 2. Strong/ Artificial General Intelligence (AGI). 3. Artificial Super Intelligence.

根据其类别，AI主要分为三种类型：1.弱/窄/人工窄智能(ANI)。 2.强大/人工智能(AGI)。 3.人工智能。

We are currently at Artificial Narrow Intelligence. If AI goes to AGI (Artificial Super Intelligence), it will change the whole human life.

我们目前在人工智能领域。 如果AI进入AGI(人工超级智能)，它将改变整个人类的生活。

AI is a core branch of Computer Science in which there is Machine learning (ML) and Deep learning (DL). Deep learning is a subset of Machine learning, while Machine learning is a subset of Artificial Intelligence.

人工智能是计算机科学的核心分支，其中有机器学习(ML)和深度学习(DL)。 深度学习是机器学习的子集，而机器学习是人工智能的子集。

In short, to be a master in AI, you have to be master in Machine learning and Deep learning as well.

简而言之，要成为AI的大师，您还必须同时拥有机器学习和深度学习的硕士学位。

现在，什么是机器学习？ (Now, What is Machine learning?)

To create a machine capable of performing a task like a human, it should have much similar intelligence as a human. This can be done by Machine learning. In simple words, we have to teach the machine, provide some data.

要创建能够执行类似人类任务的机器，它应具有与人类相似的智能。 这可以通过机器学习来完成。 简而言之，我们必须教机器，提供一些数据。

For example, to identify animals via device or any system, we have to offer numerous images of animals, provide more numbers of data for animals. We have to provides data to a machine to be capable of thinking and working.

例如，要通过设备或任何系统识别动物，我们必须提供大量的动物图像，并提供更多的动物数据。 我们必须向机器提供数据，以便能够思考和工作。

A more advanced machine required much more data to be filled it. In the beginning, Machines have to be provided with data. Afterwards, it can able to take data from the environment to learn further, inspect further.

一台更先进的机器需要更多的数据来填充它。 首先，必须为计算机提供数据。 之后，它可以从环境中获取数据以进一步学习，进一步检查。

Machine learning can be broadly classified into three types:

机器学习大致可分为三种类型：

1. Supervised learning: In this type of education, labeled data is provided to the machine. So that it gives a decision, for example, providing labeled data in the form of images of dogs to a device can quickly learn to identify dogs.

1.有监督的学习：在这种类型的教育中，将标记的数据提供给机器。 这样就可以做出决定，例如，以狗的图像形式向设备提供标记的数据可以快速学会识别狗。

Here are some of the most critical supervised learning algorithms.

以下是一些最关键的监督学习算法。

* k-Nearest Neighbors * Linear Regression * Logistic Regression * Support Vector Machines * Decision Trees and Random Forests

* k最近邻*线性回归* Logistic回归*支持向量机*决策树和随机森林

2. Unsupervised learning: In this type, Unlabelled data is provided in a group/cluster to the machine. The machine is capable of learning with this Unlabelled data.

2.无监督学习：在这种类型中，将未标记的数据以组/群集的形式提供给计算机。 机器能够学习未标记的数据。

For example, in Unsupervised learning, Workers in a company are group together according to their specifications based on some algorithms.

例如，在无监督学习中，公司的工人根据其基于某些算法的规范被分组在一起。

Some algorithms for Unsupervised learning.

某些用于无监督学习的算法。

* Clustering 1. k-Means 2. Hierarchical Cluster Analysis 3. Expectation Maximization

*聚类1. k均值2.层次聚类分析3.期望最大化

* Visualization and dimensionality reduction 1. Principal Component Analysis 2. Kernel PCA 3. t-distributed Stochastic Neighbor Embedding

*可视化和降维1.主成分分析2.内核PCA 3. t分布随机邻居嵌入

* Association rule learning 1. Apriori 2. Eclat

*关联规则学习1. Apriori 2. Eclat

3. Reinforcement learning: Reinforcement learning works in different ways. Here, the learning system is called an agent. The agent observes the environment and performs some tasks.

3.强化学习：强化学习的工作方式不同。 在这里，学习系统称为代理。 代理会观察环境并执行一些任务。

If tasks are done in the right manner/succeeded, the reward is given in return, or else penalties for the Job failed. For example, Robot uses Reinforcement learning to learn to walk.

如果以正确的方式/成功完成了任务，则作为回报，否则将对工作进行处罚。 例如，机器人使用强化学习来学习走路。

Ok, We understood AI and ML.

好的，我们了解AI和ML。

那么什么是DL？ (So What is DL?)

Deep learning is a subset of ML. It mainly deals with neural networks. It is a vast neural network concept.

深度学习是ML的子集。 它主要处理神经网络。 这是一个广阔的神经网络概念。

Due to deep learning, machines are capable of reacting in different situations; for Example, Autonomous Vehicles. It should be deal with various conditions such as understanding signals, Other vehicle distance, When to stop When to start, Where to speed up, and many more. It can be done using a broad set of labeled data and neural networks.

由于深度学习，机器能够在不同情况下做出React； 例如，自动驾驶汽车。 应该处理各种情况，例如了解信号，其他车辆距离，何时停止，何时启动，在哪里加速等。 可以使用各种标记的数据和神经网络来完成。

如何学习？ (How to learn?)

After learning a specific topic, it might confuse you at the beginning. After that, you can deal with it.

在学习了特定主题之后，一开始可能会使您感到困惑。 之后，您可以处理它。

通过学习 (Learn through)

1. Youtube videos. 2. Books. ( Best books are available to learn) 3. Udemy, Coursera, or any such related platform (They provide a certificate as well).

1. YouTube视频。 2.书籍。 (最好的书籍可供学习)3. Udemy，Coursera或任何此类相关平台(它们也提供证书)。

You have to be consistent. Provide at least 2–3 months to be perfect in AI, ML, and DL. Just starts with Deep learning and go to Machine learning and then shift to Artificial Intelligence.

您必须保持一致。 至少需要2到3个月的时间才能完美掌握AI，ML和DL。 只是从深度学习开始，然后进入机器学习，然后再转向人工智能。

Here you have to learn various categories for neural networks after that algorithm in ML then start to build a project. Software is available widely to use such as Jupyter, Tensorflow, Keras, Scikit-learn, and many more.

在ML中的该算法之后，您必须在这里学习神经网络的各种类别，然后开始构建一个项目。 软件广泛可用，例如Jupyter，Tensorflow，Keras，Scikit-learn等。

为什么所有这些东西？ (Why do all this stuff?)

Artificial intelligence is one of the fields that makes you billionaire, by Job or by a Startup. The more advanced sector requires more knowledge and then leads to more money. The whole world is concentrating on AI, Researching in various fields. Making autonomous vehicles is a good example. So if you want to go to the Technology sector, it is one of the top skills that you have to learn.

人工智能是让乔布斯或初创企业成为亿万富翁的领域之一。 更先进的部门需要更多的知识，然后才能赚更多的钱。 全世界都在关注AI，研究各个领域。 制造自动驾驶汽车就是一个很好的例子。 因此，如果您想进入技术领域，这是您必须学习的最重要技能之一。

Originally published at https://www.shitus.com.

最初在 https://www.shitus.com上 发布 。