There is always a routine that we follow in our life. If our body is a biological computer , then we follow the following code.

在我们的生活中，总有一个套路可以遵循。 如果我们的身体是一台生物计算机，那么我们将遵循以下代码。

While(life != dead){    life.wake_Up_In_The_Morning();    life.eat();    life.work(`easy `, `less time `, `aim: money `);    life.sleep();}after_Death("Even your aim did not follow your grave")

But we must not forget that we were the developers of this code. So it’s in our hands to change it. It’s very important for us to keep ourselves motivated and work on jobs that are close to our heart. So, before getting into AI we must know what are all the things that AI does in today’s world. Hence before getting into what we do in AI we must have a serious dedication and love towards this domain.

但是我们一定不能忘记我们是这段代码的开发者。 因此，改变它就在我们手中。 对于我们而言，保持动力并从事与我们内心最接近的工作非常重要。 因此，在进入AI之前，我们必须了解AI在当今世界中所做的所有事情。 因此，在进入我们在AI中所做的工作之前，我们必须对此领域有认真的奉献和热爱。

The very bad practice most of us do is take a Github code which does some AI job like Computer Vision/ Speech Recognition / Predictive Analysis etc.., and run it in our system and call ourselves as AI Engineer. No, this kind of habit helps us to do projects, but never improves our knowledge in AI. The main reason for this is, many people consider AI as just a complex Python code that can produce results. No, Python is just a programming language. We use this language(even others are used, but less common) to implement an AI system to produce results. So there’s a lot more work that is going behind this Python script.

我们大多数人最糟糕的做法是采用Github代码来完成一些AI工作，例如计算机视觉/语音识别/预测分析等，然后在我们的系统中运行它并称自己为AI工程师。 不，这种习惯可以帮助我们进行项目，但从不提高我们对AI的知识。 造成这种情况的主要原因是，许多人认为AI只是可以产生结果的复杂Python代码。 不，Python只是一种编程语言。 我们使用这种语言(甚至使用其他语言，但不常见)来实现AI系统以产生结果。 因此，此Python脚本背后还有很多工作要做。

To be a successful AI Engineer doesn’t mean we need to be strong in Coding.

成为一名成功的AI工程师并不意味着我们需要精通编码。

什么是AI，它与其他有什么不同？ (What is AI and how is it different from others ?)

AI is considered to have the following sub domains as shown in the figure:

如图所示，AI被认为具有以下子域：

Yes, with today’s resources and technology we cannot create an entire system that does like a human. So, we need to know what we can do and what we won’t be able to. So the traditional way to start with AI is to go on with Machine Learning concepts . Each and every concept is well adhered to its logic. So Machine Learning is a great kick starter to get into the field of AI.

是的，利用当今的资源和技术，我们无法创建像人类一样的整个系统。 因此，我们需要知道我们能做些什么以及我们将做不到的。 因此，从AI开始的传统方法是继续学习机器学习概念。 每个概念都严格遵守其逻辑。 因此，机器学习是进入AI领域的绝佳起点。

Once the traditional Algorithms of ML is done, we need to get in Deep Learning. Today the entire AI Society is fond of this Domain. This field has lot of concepts , where we need to understand each and every concept starting from its root. Yes, in order to get deeper , it’s take a lot of time.

一旦完成了传统的ML算法，我们就需要进行深度学习。 今天，整个AI协会都喜欢这个领域。 这个领域有很多概念，我们需要从它的根源开始理解每个概念。 是的，要更深入，这需要很多时间。

Stay patient and trust your journey

保持耐心并信任您的旅程

Both ML and DL are defined as “the work done by a machine without being explicitly coded”. It means the ability of Machine to work based on its learning. And that is all about AI

ML和DL均被定义为“ 由机器完成的工作，无需显式编码 ”。 它表示机器根据其学习而工作的能力。 这就是关于AI的全部

如何开始这些概念？ (How to start these Concepts ?)

Whenever we talk about these concepts, we all must keep one thing in mind. What is the input am feeding and what is the output am expecting? Life becomes simple when we understand each and every concept in this fashion. We must never forget that Computer can study only numbers. So keeping this in mind, we need to see how to feed an input in order to receive an output.

每当我们谈论这些概念时，我们都必须牢记一件事。 输入是什么，输出是什么？ 当我们以这种方式理解每个概念时，生活就会变得简单。 我们永远不会忘记，计算机只能学习数字。 因此，请记住这一点，我们需要了解如何馈送输入以接收输出。

A bit of coding knowledge is enough to implement huge AI systems

一定的编码知识足以实现庞大的AI系统

It’s better to implement the code simultaneously when we learn each concept in ML/ DL. So once we learn the concept, the next step should be implementing the code. This cycle of approach is a best way which helps us to learn the theory and also practically check the results.

当我们学习ML / DL中的每个概念时，最好同时实现代码。 因此，一旦我们学习了概念，下一步应该是实现代码。 这种方法的循环是最好的方法，可以帮助我们学习理论并实际检查结果。

In order to survive in this field , it’s a must to study Scientific papers and journals. It’s a very true fact that Scientific papers cannot be read in a single shot. The authors of the paper would have spent at least a year to write such complex publications. So the least respect we could do is to study them patiently. This method of learning concepts from the way it originated gives us deeper intuitions and helps us to explore minute details of the topic.

为了在该领域生存，必须研究科学论文和期刊。 这是一个非常真实的事实，无法一次性阅读科学论文。 该论文的作者将花费至少一年的时间编写如此复杂的出版物。 因此，我们最不应该做的就是耐心地研究它们。 这种从概念的起源中学习概念的方法为我们提供了更深刻的直觉，并有助于我们探索该主题的细节。

完成学习AI需要多长时间？ (How long does it take to finish learning AI ?)

It’s a never ending Topic. We need to fix in mind that it is not a course to complete in time.

这是一个永无止境的话题。 我们需要牢记，这不是及时完成的​​课程。

We are not preparing for any exams to think about it’s timeline. But of course we can reframe the above question as “In what speed can I go in learning concepts ?”. This entirely depends upon the individual. For few people based on the concept it takes 2 days to finish . Few people take a week to finish. Some might take more than 2 weeks. The goal here is not to finish something in a particular time. The goal here is to finish the entire topic taking any amount of significant time.

我们没有为任何考试做准备以考虑时间表。 但是我们当然可以将以上问题重新定义为“我可以以多快的速度学习概念？”。 这完全取决于个人。 对于基于该概念的少数人来说，需要2天才能完成。 很少有人花一个星期才能完成。 有些可能需要2个多星期。 这里的目标不是在特定时间内完成某件事。 这里的目标是花费大量时间完成整个主题。

我是否需要等到完成概念的整个学习？ (Should I need to wait until I complete the entire study of Concepts?)

No, we all learn from mistakes. So while we learn, it’s also better to do a project on what we have learnt. Supposing we have learnt CNN partially, but need to work in a project that classifies fruits based on images, it’s always better to give a shot. This is because, once we try to implement on real time scenarios, we can try rectifying our mistakes and thus enabling us to achieve the desired output. Also, while implementing projects, it’s adds to our experience and also boosts our confidence.

不，我们都从错误中学习。 因此，在我们学习的同时，最好根据我们所学的知识来做一个项目。 假设我们已经部分地学习了CNN，但是需要在一个基于图像对水果进行分类的项目中进行工作，那么总能出手更好。 这是因为，一旦我们尝试在实时场景中实施，就可以尝试纠正错误，从而使我们能够获得所需的输出。 此外，在实施项目时，它增加了我们的经验，也增强了我们的信心。

If we could follow a proper way and love what we do, we could change the code of our system(which we spoke at the very beginning of this Article)as follows:

如果我们可以遵循正确的方式并且热爱我们所做的事情，那么我们可以更改系统代码(在本文开头就谈到)，如下所示：

While(life != dead){    life.wake_Up_In_The_Morning();    life.eat();    life.work(`hard_work`, `patience `,`aim: happiness and peace`);    life.code();    life.sleep();}after_Death("Let the world speak about you");

结论 (Conclusion)

I have shared few ways on how to encounter this AI field. It’s truly based on my experience on learning each and every concept. Even I was confused in the beginning. But as time passed I was able to find the flow and the path to crack it. I wanted to share my experience to others so that it would be easy for others who aspire to be a part of this AI Society. It’s absolutely fine if we don’t understand a topic. Time is the best solution for it. All the Best !!!

关于如何遇到这个AI领域，我分享了几种方法。 这完全基于我在学习每个概念上的经验。 甚至一开始我也很困惑。 但是随着时间的流逝，我能够找到破解它的流程和途径。 我想与他人分享我的经验，以便对渴望成为这个AI学会一部分的其他人来说很容易。 如果我们不理解某个主题，那绝对没问题。 时间是最好的解决方案。 祝一切顺利 ！！！