As stated earlier in the subtitle, time and costs are big hinderances people face when attempting to learn Data Science, Artificial Intelligence or Machine Learning. Self-learning is an art that requires discipline, dedication and discipline to master. It gives you the flexibilty to couple learning with work or school if mastered properly. However, when starting to learn Data Science, AI or ML, the beginning stages are very tough but trust me when I say it is worth the worry. The key to making good progress when learning by yourself is to study at your own pace. In this story, I am going to share a path which people seeking to learn Data Science, AI and ML can follow and make good progress at learning new things. I will also share links to resources I have personally used and can recommend without a doubt.

如小标题前面所述，时间和成本是人们在尝试学习数据科学，人工智能或机器学习时面临的重大障碍。 自我学习是一门需要纪律，奉献和纪律才能掌握的艺术。 如果掌握得当，它可以使您灵活地将学习与工作或学校相结合。 但是，当开始学习数据科学，AI或ML时，开始阶段非常艰难，但是当我说值得担心时，请相信我。 独自学习时取得良好进步的关键是按照自己的进度学习。 在这个故事中，我将分享一个寻求学习数据科学，人工智能和机器学习的人们可以遵循的道路，并在学习新事物方面取得良好的进步。 我还将分享到我个人使用的资源的链接，毫无疑问可以推荐。

学习一些数学 (Learn Some Math)

As annoying as this sounds, it is very essential in this field. I think it is safe to assume that anyone reading this has some basic to intermediate knowledge in mathematics from high school. It’s a good start but its nothing close to enough in the the field of Data Science, AI and ML. You would need to dive deep a little further and learn some concepts in statistics, algebra and other topics. I would compile a list of topics and resources to help you study math for data science but it has already been perfectly done in this article by Ibrahim Sharaf ElDen.

听起来很烦人，但在这个领域中非常重要。 我认为可以肯定地说，任何阅读此书的人都具有高中数学的一些基础知识或中级知识。 这是一个好的开始，但在数据科学，人工智能和机器学习领域还远远不够。 您需要进一步深入研究，并学习一些统计，代数和其他主题的概念。 我将整理一份主题和资源列表，以帮助您学习数据科学的数学，但是Ibrahim Sharaf ElDen在本文中已经完美地做到了。

学习编码 (Learn To Code)

As a beginner, do not jump straight into learning to write code for machine learning but rather, learn the core concepts of programming in general. Learn what programming is all about, learn the types of code that exists and how to properly write code. This is very important because you will learn many essential concepts that you’ll live with for the rest of your life in this field. Take it slow in this step, do not rush into learning advanced things, understanding most of the things in this step will determine how good you will be in the industry. You can find a very good video resource which introduces you to programming and computer science here. This video takes you through all the important concepts of programming and computer science in general. Take your time with it and make sure you understand every bit of it.

作为初学者，不要直接进入学习编写用于机器学习的代码的过程，而应该学习一般编程的核心概念。 了解编程的全部内容，了解现有的代码类型以及如何正确编写代码。 这一点非常重要，因为您将学习该领域余生中将生活的许多基本概念。 在此步骤中花点时间，不要急于学习高级的东西，了解此步骤中的大多数内容将决定您在行业中的表现。 你可以找到它向您介绍了编程和计算机科学的一个很好的视频资源在这里 。 该视频将带您全面了解编程和计算机科学的所有重要概念。 花些时间，并确保您了解它的每一点。

使自己适应编程语言 (Make Yourself Comfortable With A Programming Language)

There are many languages Data Scientists, Artificial Intelligence and Machine Learning Engineers use today to get their work done with the most used languages being Python, R, Java, Julia and SQL. Many other languages can be used but the ones I listed are the most used languages for a number of reasons.

如今，数据科学家，人工智能和机器学习工程师使用多种语言来完成其工作，其中最常用的语言是Python，R，Java，Julia和SQL。 可以使用许多其他语言，但出于多种原因，我列出的语言是最常用的语言。

• They are easy to learn and fast in development if one dedicates enough time to learn and remains consistent.如果人们投入足够的时间学习并保持一致，那么它们就容易学习并且发展Swift。
• They let you do more with less code.他们让您用更少的代码做更多的事情。
• They have a good and solid community built around them to help and support you anytime you encounter any problems.他们周围有一个良好而牢固的社区，可以在遇到任何问题时为您提供帮助和支持。
• They have almost all the libraries and packages you will ever need in your work as a Data Scientist, AI or ML Engineer.他们拥有您作为数据科学家，人工智能或机器学习工程师所需的几乎所有库和软件包。
• They are open source and free to use.它们是开源的，可以免费使用。

There is absolutely nothing wrong with learning more than one language, as a matter of fact, it is a good thing to know more than one language. However, you have to take your time when learning programming languages and try as much as you can not to learn more than one language simultaneously as this may confuse you and leave you disoriented for a while. Take your time, learn one language at a time and make sure to learn only the parts of a language you need for your career. I would suggest you learn python before any other language as it is a relatively easy language to understand. I would also recommend you learn general python before you go into learning python for data science and AI/ML.

学习一种以上的语言绝对没有错，事实上，了解一种以上的语言是一件好事。 但是，您必须花时间学习编程语言，并尝试尽可能多地学习多种语言，因为这可能会使您感到困惑，并使您暂时迷失方向。 花时间，一次学习一种语言，并确保仅学习职业所需的语言部分。 我建议您先学习python，因为它是一种相对容易理解的语言。 我还建议您在学习数据科学和AI / ML的 python之前先学习一般的python 。

了解如何获取数据 (Learn How to Get Data)

Data will most often not be handed to you dierctly, sometimes there will be no data for you at all but whatever be the case, you have to find a way to get data you can work with. The organization you work with may have a good data collection system, if they do, thats a plus for you. If not, you have to find a way to get data, not just any data but good data you can work with to achieve your goal. Getting data does not directly imply to mining data, it is a process under data mining. You can get free and open source data in many places on the internet and you may sometimes have to scrape data from websites. Web scraping is very important and I beseech everyone to learn it as a need for it may arise in your career as a Data scientist, AI or ML engineer. Here is a good tutorial on web scraping. Data may also be kept in a database so as a Data scientist, AI or ML engineer, you have to know a bit of database administration so as to be able to connect to and work directly from a database. Knowledge in SQL is very essential in this stage. You can learn SQL for Data Science here.

数据通常不会直接发送给您，有时根本没有数据供您使用，但是无论如何，您都必须找到一种获取可以使用的数据的方法。 与您合作的组织可能有一个很好的数据收集系统，如果有的话，那对您来说是个加分。 如果没有，您就必须找到一种获取数据的方法，不仅可以获取任何数据，还可以获取可以用来实现目标的良好数据。 获取数据并不直接意味着挖掘数据，它是数据挖掘下的一个过程。 您可以在Internet上的许多地方获得免费和开源的数据，有时您可能不得不从网站上抓取数据。 Web抓取非常重要，我恳请所有人学习它，因为在您作为数据科学家，AI或ML工程师的职业生涯中可能会需要它。 这是有关网络抓取的好教程。 数据也可能保存在数据库中，因此作为数据科学家，AI或ML工程师，您必须了解一些数据库管理知识，以便能够连接到数据库并直接从数据库进行工作。 在此阶段，SQL知识非常重要。 您可以在此处学习SQL for Data Science。

了解如何处理数据 (Learn How To Process Data)

This is most often called Data Wrangling. This process involves cleaning the data you have, this can be done by performing some Exploratory Data Analysis on the data you have and prunning out the unwanted parts of your data. The process also involves structuring the data you have into a form that you can work with. This stage is the most exhausting part of working on a project in Data Science, Artificial Intelligence or Machine Learning. In the learning process, most of the sample data you are going to work with have been pre-processed but data in the real world might have not been through any stage of processing. As someone aspiring to be good at what you do in this field, you should find some real world data and work around it. Real world data can be found almost anywhere but Kaggle is an amazing place to get real world data from companies around the globe. Data wrangling or processing is an extremely tiring task but with constant dedication and consistency, it can be a very interesting thing for you. You can find an amazing lecture on Data Wrangling by a Cambrige professor here.

这通常称为数据整理。 此过程涉及清除您拥有的数据，这可以通过对您拥有的数据执行一些探索性数据分析并删除数据中不需要的部分来完成。 该过程还涉及将您拥有的数据构造为可以使用的形式。 这个阶段是从事数据科学，人工智能或机器学习项目的最疲惫的部分。 在学习过程中，您将使用的大多数示例数据都已经过预处理，但是现实世界中的数据可能尚未经过任何处理阶段。 作为一个渴望在此领域做得很好的人，您应该找到一些现实世界的数据并加以解决。 几乎可以在任何地方找到真实世界的数据，但是Kaggle是从全球公司获取真实世界数据的绝佳场所。 数据整理或处理是一项非常艰巨的任务，但是由于始终如一的奉献精神和一致性，对您而言这可能是一件非常有趣的事情。 您可以在此处找到Cambrige教授关于数据整理的精彩演讲。

了解如何可视化数据 (Learn How To Visualize Data)

Being a Data Scientist, AI or ML engineer doesn’t necessarily mean everyone at your workplace or in your team will be able to understand the technicalities in your field or will be able to make inferences from data in its raw form. This is why there is a need to learn to visualize data. Data Visualization basically refers to the process of presenting data in a graphical form in order to give anyone regardless of their knowledge in data science, AI or ML an understanding of what the data is about. There are many ways of visualizing data. As programmers, writing code to visualize data should be our go-to method as this is fast and comes with no costs. Writing code to visualize data can be done with many of the free and open source libraries that come with the programming languages we use. Matplotlib, Seaborn and Bokeh are all python libraries we can use to visualize data. You can find a video tutorial on data visualization with matplotlib here. Another way of visualizing data is using closed-source tools such as Tableau. There are many closed-source tools out there for data visualization and they are used to make more elegant and complex visualisations but they come at a cost. Tableau is the most common one and it is a tool I personally use very often. Learning to use Tableau is something I would recommend to everyone. You can find a very good tutorial on using Tableau here.

作为数据科学家，AI或ML工程师，不一定意味着您的工作场所或团队中的每个人都将能够理解您所在领域的技术或能够从原始格式的数据中进行推断。 这就是为什么需要学习可视化数据的原因。 数据可视化基本上是指以图形形式显示数据的过程，以便使任何人，无论他们在数据科学，人工智能或机器学习方面的知识如何，都可以理解数据的含义。 有很多可视化数据的方法。 作为程序员，编写代码以可视化数据应该是我们的首选方法，因为这是快速且无成本的。 可以使用我们使用的编程语言附带的许多免费和开源库来编写代码以可视化数据。 Matplotlib，Seaborn和Bokeh都是我们可以用来可视化数据的python库。 您可以在此处找到有关使用matplotlib进行数据可视化的视频教程。 可视化数据的另一种方法是使用诸如Tableau之类的闭源工具。 那里有许多用于数据可视化的闭源工具，它们被用来进行更优雅，更复杂的可视化，但要付出一定的代价。 Tableau是最常见的工具，它是我个人经常使用的工具。 我会建议所有人学习使用Tableau。 您可以在此处找到有关使用Tableau的很好的教程。

人工智能与机器学习 (Artificial Intelligence And Machine Learning)

Artificial Intelligence and Machine learning are more like subsets of Data Science because they are powered by data. They refer to the process involved in training machines or other inanimate objects to behave like human beings by feeding them with well-processed data. Machines can be taught to do many things human beings can do by gradually teaching and guiding them. Think of machines in this case as babies who have no knowledge at all but are gradually taught to identify objects, speak, learn from mistakes and become better. Machines can also be taught in the same way to do most of those things. AI and ML is basically bringing life to machines by use of many mathematical algorithms. The full potential of Artificial Intelligence and Machine learning is still not known since it is one of the fields that is constantly being improved. But for now, AI and ML is widely used for cognitive functions such as Object Detection and Recognition, Facial Recognition, Speech Recognition and Natural Language Processing, Fraud and Spam detection and others. I will leave the In-depth walk-through of AI and ML for another story but for now, I would like you to learn about the general applications of AI and ML here and dive deep into machine learning from this video resource. By the end of these videos you should have gathered a beginner to intermediate knowledge about machine learning, learnt about the many machine learning algorithms available, what they do and how to use them. You should by now be ready to build your first simple machine learning model, which you can do with help from this article by Victor Roman.

人工智能和机器学习更像是数据科学的子集，因为它们由数据驱动。 他们指的是训练机器或其他无生命物体所涉及的过程，即通过向它们提供经过良好处理的数据来使其表现得像人。 通过逐步教授和指导机器，可以教机器做人类可以做的许多事情。 在这种情况下，将机器想象为完全没有知识，但是逐渐学会识别物体，说话，从错误中学习并变得更好的婴儿。 也可以用相同的方式教机器来完成大多数这些事情。 AI和ML基本上是通过使用许多数学算法使机器栩栩如生。 人工智能和机器学习的全部潜力仍然未知，因为它是不断改进的领域之一。 但是目前，AI和ML已广泛用于认知功能，例如对象检测和识别，面部识别，语音识别和自然语言处理，欺诈和垃圾邮件检测等。 我会离开的深入步行通过AI和ML的另一个故事，但现在，我想你了解AI和ML的一般应用在这里和潜水从深入学习机这一视频资源 。 在这些视频的结尾，您应该已经使初学者掌握了有关机器学习的中级知识，了解了许多可用的机器学习算法，它们的作用以及如何使用它们。 现在，您应该已经准备好构建第一个简单的机器学习模型，您可以在Victor Roman的 这篇文章的帮助下进行操作。

了解如何使您的机器学习模型可用于Internet (Learn How To Make Your Machine Learning Models Available For Use On The Internet)

Models you build using machine learning can be made available to everyone on the internet by deploying them. To do this, a good understanding of web development is needed as you would have to create a webpage or a group of webpages to house your model. The front-end of your website would also need to communicate with the back-end which contains your model. For this, you’d also need to know how to build and integrate APIs to handle the communication between the front-end of your website and the backend which houses your machine learning model. You may need a good understanding of cloud computing and DevOPs if you intend to deploy your machine learning models on a cloud server, through a pipeline or docker container. There are many ways of deploying machine learning models but for a start, I suggest you learn how to deploy machine learning models using the python web framework, Flask. A good tutorial for doing that can be found here.

通过机器学习，您可以将使用机器学习构建的模型提供给互联网上的每个人。 为此，您需要对Web开发有一个很好的了解，因为您必须创建一个网页或一组网页来容纳模型。 您网站的前端还需要与包含模型的后端进行通信。 为此，您还需要知道如何构建和集成API，以处理网站的前端与包含机器学习模型的后端之间的通信。 如果您打算通过管道或docker容器在云服务器上部署机器学习模型，则可能需要对云计算和DevOP有很好的了解。 部署机器学习模型的方法有很多，但首先，我建议您学习如何使用python网络框架Flask部署机器学习模型。 可以在这里找到一个很好的教程。

找一个导师 (Get yourself a Mentor)

Learning on your own is amazing but nothing beats learning directly from a professional in the industry. This is because you get to learn real life concepts and other things only hands-on experience can teach. There are many benefits with having a mentor but not every mentor can be impactful in your career or life as a whole. That is why it is important to have a good mentor. Notitia AI is a good platform for being trained from beginner to expert level in Data Science, Artificial Intelligence and Machine Learning where you will be assigned a personal Mentor who will personally and professionally contibute to your growth in the chosen field. Its is also the most affordable virtual training and mentorship platform you can find out there.

自己学习是惊人的，但是没有什么比直接向行业专业人士学习还好。 这是因为您将学习现实生活中的概念和其他只有实践经验才能教授的东西。 拥有一名导师有很多好处，但并不是每位导师都能对您的职业生涯或整个人生产生影响。 这就是为什么拥有一个好的导师很重要的原因。 Notitia AI是一个很好的平台，可以在数据科学，人工智能和机器学习方面从初学者到专家水平进行培训，在该平台上，您将获得个人导师，他们将亲自和专业地协助您在所选领域的发展。 它也是您在那里可以找到的最实惠的虚拟培训和指导平台。

Note that just taking courses and learning from online resources will not make you a Data Scientist, AI or ML Engineer. You will have to get certified by institutions that offer certifications in these fields and some job positions will also require you to have some academic degrees. Take your time to learn, earn your certifications or degrees and you will be industry ready.

请注意，仅参加课程并从在线资源中学习不会使您成为数据科学家，AI或ML工程师。 您将必须获得在这些领域提供认证的机构的认证，并且某些工作职位还需要您具有一定的学位。 花些时间学习，获得证书或学位，您将做好行业准备。

I hope you liked this story and it has been helpful to you. Thanks for making time to read this. Kindly share your thoughts and opinions on this story and feel free to correct me at places you think I have gone wrong. I am human and as such, I am prone to making mistakes. You can reach me directly on Twitter or LinkedIn. You can also contact me to suggest topics you’d like me to write about in my consequent stories. Have a wonderful day.

希望您喜欢这个故事，对您有所帮助。 感谢您抽出宝贵时间阅读本文。 请分享您对这个故事的想法和观点，并随时在您认为我错了的地方纠正我。 我是人类，因此我容易犯错误。 您可以直接在Twitter或LinkedIn 上与我联系。 您也可以与我联系，以建议您希望我在随后的故事中写的主题。 祝你有美好的一天。

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。