No Matter What People Tell You, Words And Ideas Can Change The World. — Robin Williams
Beginners in this field have become accustomed to reinventing existing projects as part of their portfolio projects and this is not a good thing. What makes you unique out of the hundreds of learners with the same projects you have built? What makes you stand out as a data scientist, AI, or ML engineer? In this article, I am going to share with you some tips on ‘generating’ your data science, AI, or ML projects to appear unique at what you do.
该领域的初学者已经习惯于将现有项目重塑为他们的投资组合项目的一部分，这不是一件好事。 是什么让您从拥有相同项目的数百个学习者中脱颖而出？ 是什么让您脱颖而出成为数据科学家，人工智能或机器学习工程师？ 在本文中，我将与您分享一些有关“生成”数据科学，AI或ML项目以使您所做的工作显得独特的提示。
重新发明，但有风格 (Re-invent, But With Style)
This point aims at exploring your innovative skills. Being an innovator is a very essential skill required of every data scientist, AI, or ML engineer. Your ability to take an existing solution and develop it more to yield better results or insights will go far to help your career as a data scientist, AI, or ML engineer. For instance, you can take the popular project, ‘Predicting Boston House Prices’ and rebuild this project to be true for your specific or current city. Test your innovative skills, explore new datasets, use better algorithms, and tune hyper-parameters to fit your model more perfectly than it currently is. By doing these things to existing projects, you give them a new feel and create an image of professionalism.
这一点旨在探索您的创新技能。 成为创新者是每位数据科学家，AI或ML工程师必备的非常重要的技能。 您采用现有解决方案并进行更多开发以产生更好的结果或见解的能力将极大地帮助您成为数据科学家，AI或ML工程师。 例如，您可以采用流行的项目“预测波士顿房屋价格”，然后重新构建此项目以使其适合您的特定或当前城市。 测试您的创新技能，探索新的数据集，使用更好的算法以及调整超参数以使其模型比当前更完美。 通过对现有项目执行这些操作，您可以给他们带来新的感觉并创建专业形象。
If you want something new, you have to stop doing something old.–Peter F. Drucker
如果您想要新的东西，就必须停止做旧的事情。–Peter F. Drucker
召唤您的内在创造力自我 (Summon Your Inner Creative Self)
Creativity involves breaking out of expected patterns in order to look at things in a different way. — Edward de Bono
To come up with awesome and unique AI, ML, or data science ideas, one must be creative and willing to attempt things that have never been done. You should have the desire to birth something that most people will tag as ‘absurd’ or ‘impossible’. Yes, sometimes some ideas are rather absurd and aren’t worth the time, but here are a few ways you can come up with ideas that are worth the try.
为了提出很棒的独特AI，ML或数据科学构想，必须有创造力并且愿意尝试从未完成的事情。 您应该希望出生一些被大多数人称为“荒谬”或“不可能”的东西。 是的，有时有些想法很荒谬，不值得花时间，但是这里有几种方法可以提出值得尝试的想法。
“what if I could make…”
This is the most common way to come up with an idea. It generally signifies you have an existing problem and you are thinking of a way to solve it. In this case, before you go any further, do some background checks on the problem and check to see if there are currently existing solutions. If there are solutions that will perfectly solve your problem, there would be no need to go further. If there aren’t, see how best you can tackle the problem.
这是提出想法的最常用方法。 它通常表示您有一个现有的问题，并且正在考虑解决的方法。 在这种情况下，在进行任何进一步的操作之前，请对问题进行一些背景检查，并检查是否存在当前的解决方案。 如果有解决方案可以完美解决您的问题，则无需走更远的路。 如果没有，请查看如何最好地解决该问题。
“This would work better if it were like this instead”
Here, you know of an existing solution and are seeking to make it better in a way or two. Go for it, don’t let the existence of an existing solution stop you from making it better. Give it the best you’ve got, the odds are there might be hundreds of people with your same problem and you might end helping so many people by taking that project. In my subsequent stories, I’ll talk about how I came up with the idea that led to writing my first python machine learning package.
在这里，您了解现有的解决方案，并且正在寻求以一两种方式进行改进。 努力吧，不要让现有解决方案的存在使您无法使其变得更好。 尽力而为，就可能有成百上千的人遇到同样的问题，而您可能最终会通过参加该项目而帮助那么多人。 在接下来的故事中，我将讨论如何提出导致编写第一个python机器学习包的想法。
检查可行性 (Check The Feasibility)
This is probably the most important thing to consider before starting work on any project. You need to know whether the project idea is executable. Ask yourself a few questions concerning your idea before you kickstart implementation.
在任何项目上开始工作之前，这可能是最重要的考虑因素。 您需要知道项目构想是否可执行。 在开始实施之前，请问自己一些有关您的想法的问题。
Does My Idea Solve A Genuine Problem?
Unless you are building a project for the fun of it, every project you build should aim at solving a genuine problem. A problem that is worth solving and would make an impact on the existing methodology. If your answer to this question is yes, you can go ahead to answer the next question.
除非您是出于乐趣而构建项目，否则构建的每个项目都应着眼于解决真正的问题。 一个值得解决的问题，它将对现有方法产生影响。 如果您对这个问题的回答是“是”，则可以继续回答下一个问题。
Is My Idea Possible To Implement With The Current Technologies At Hand?
Even though Artificial Intelligence is at a very advanced and futuristic level today, there is still so much more we can do but are limited because of the unavailability of certain forms of technology. In cases like this, all we can do is to develop theories and concepts with the hope that technology will soon catch up with us and our theories and concepts can be made practical.
Do I Have The Required Skills To Build This Project?
It is very important to consider your current skill set before you start implementing a project idea. If you lack the required skills, you can dedicate some time to learn the necessary skills. You could also team up with a group of like-minded people and work together on building that project.
在开始实施项目构想之前，考虑当前的技能非常重要。 如果您缺少必要的技能，则可以花一些时间来学习必要的技能。 您也可以与一群志趣相投的人合作，共同建设该项目。
An idea that is developed and put into action is more important than an idea that exists only as an idea.– Buddha
It is tough to get your idea into production, but it is not impossible. Believe in your idea even if you are the only one who does. Work hard to achieve your goals and you will definitely be successful.
很难将您的想法付诸实践，但这并非不可能。 即使您是唯一的人，也要相信您的想法。 努力实现目标，您一定会成功。
Thank you for making time to read this story. I hope you learned something new and it has been helpful. You are welcome to share your thoughts and opinions in the response section and you can contact me directly on Twitter or LinkedIn. Happy Hacking!
A big thank you to Anna Ayiku for proofreading and correcting the many mistakes I made writing this.
非常感谢 Anna Ayiku 校对并纠正了我在撰写本文时犯下的许多错误。