我想学习人工智能和机器学习,我可以从哪里开始呢（二）
I want to learn artificial intelligence and machine learning. Where can I start?
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网友：人们应该如何开始学习人工智能和机器学习？人工智能是主要领域，而机器学习是子领域。因此，对于人工智能，我建议你去 麻省理工学院的公开课网站上看看......
正文翻译
I want to learn artificial intelligence and machine learning. Where can I start?
我想学习人工智能和机器学习,我可以从哪里开始呢?
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How should one start learning about AI and machine learning?
AI is main field,while ML is subfield of AI.
So for AI , I would suggest to go with MIT course on their opencourseware site. You can also check udacity's nanodegree or Udacity's Artificial Intelligence course taught by Sebastian Tharun(sorry if I misspelled it). Besides that you can read books on AI. Artificial Intelligence :a modern approach, is a great book. Many people recommend it.
人们应该如何开始学习人工智能和机器学习？
人工智能是主要领域，而机器学习是子领域。
因此，对于人工智能，我建议你去 麻省理工学院的公开课网站上看看。你也可以查看塞巴斯蒂安·塞伦（Sebastian Tharun）教授的在优达学城平台上的纳米学位或人工智能课程。此外，你还可以阅读有关人工智能的书籍。《人工智能：现代方法》是一本很棒的书，很多人都推荐它。
Start with Coursera’s machine learning course by Stanford university. It's really great place to start for beginners in ML. It will teachs you everything necessary to know about ML. After that learn how to use python for machine learning. Make some project using it because coding is only thing that matters a lot in ML or AI. If you want to learn mathematical proofs and want to dive into maths behind ML then go with YouTube serie of video lectures by Stanford. Note they are not for beginners.
Beside it I would suggest to read lot of blogs on net. Use people's code to see how to implement algorithms. Learn how to code good AI,ML models and how to uate algorithms.
After 3 months in ML, start to complete on Kaggle. It's really good place to learn new tricks in ML. Read blogs and interviews of grandmasters on Kaggle, it will increase your knowledge as well as confidence.
Hope it helps.
Happy learning.
对于机器学习
从斯坦福大学的Coursera平台开始学习机器学习课程。对于机器学习方面初学者来说，这真的是一个很好的起点。它将教你了解机器学习的所有必要知识。之后，学习如何使用python进行机器学习。使用它制作一些项目，因为编码是ML或AI中唯一重要的事情。如果你想学习数学证明，想深入学习ML背后的数学，那么就去看斯坦福大学的油管系列视频讲座。请注意，它们不适合初学者。
除此之外，我还建议在网上读很多博客。使用人们的代码来了解如何实现算法。学习如何编写好的AI、ML模型以及如何评估算法。
学习机器语言3个月后，开始在Kaggle平台上完成学业。这是学习ML新技巧的好地方。阅读Kaggle平台上的博客和大师访谈，这将增加你的知识和信心。
希望有帮助。
快乐学习哈。
Artificial Intelligence (AI) and Machine Learning will be here for the long term. The majority of industry verticals harness AI and ML to create multiple job opportunities and a better future.
Recent innovations such as intelligent voice assistants and self-driving vehicles, robotic process automation, and so on have helped ML and AI gain traction. This has taken the world by storm, and everyone is eager to learn more about it.
AI and ML are in constant change and evolving daily. A few universities offer a formal degree, but you can achieve it in many other ways.
I have compiled the most effective ways to learn AI/Machine Learning after extensive research.
My best tip for learning AI and machine learning is to follow a 5-step process.
人工智能（AI）和机器学习（ML）将长期存在。大多数垂直行业利用AI和ML创造多个就业机会和更好的未来。
最近的创新，如智能语音助手和自动驾驶车辆、机器人流程自动化等，帮助ML和AI获得了吸引力。这场风暴席卷了世界，每个人都渴望了解更多。
AI和ML每天都在不断变化和发展。一些大学提供正式学位，但你可以通过许多其他方式获得。
经过广泛的研究，我整理了学习AI/ML的最有效方法。
我学习人工智能和机器学习的最佳建议是遵循5步流程：
Change your mindset. You can apply machine learning and AI to your everyday life.
Question yourself, What is Holding you Back From Your Machine Learning Goals?
Why Machine Learning and AI Does Not Have to Be So Hard
How to Think About AI and Machine Learning
Find Your Machine Learning and AI Tribe
Step 2:
Choose a process and use a systemic approach to problem-solving. To solve problems, use a systematic approach.
Applied Machine Learning Process
步骤1：
改变你的心态。你可以将机器学习和人工智能应用到你的日常生活中。
扪心自问，是什么阻碍了你实现机器学习目标？
为什么机器学习和人工智能不必如此困难
如何思考人工智能和机器学习
找到一大群人一起学习机器学习和人工智能
第2步：
选择一个过程并使用系统的方法解决问题。要解决问题，请使用系统的方法。
应用机器学习过程
原创翻译：龙腾网 http://www.ltaaa.cn 转载请注明出处
Choose a tool. Choose a tool that is appropriate for you and map it onto the process.
Beginners: Weka Workbench.
Intermediate: Python Ecosystem.
Advanced: R Platform.
sext the Best Programming Language for Machine Learning and AI that helps you at any level.
Step 4:
Use datasets to your advantage and practice the process. Choose datasets that you would like to use and then practice the process.
Practice Machine Learning with Small In-Memory Datasets
Tour of Real-World AI and Machine Learning Problems
Work on AI and Machine Learning Problems That Matter To You
第3步：
选择一个工具。选择适合你的工具，并将其映射到流程中。
初学者：Weka Workbench平台。
中级：Python生态系统。
高级：R平台。
选择机器学习和人工智能的最佳编程语言，可以在任何水平上帮助到你。
第4步：
根据自己的优势使用数据集，并练习该过程。选择要使用的数据集，然后练习该过程。
使用小内存数据集练习机器学习
处理现实世界的人工智能和机器学习方面难题之旅
研究对你至关重要的人工智能和机器学习方面的难题
Get Paid by Applying for an appropriate job
Start and maintain a good career with great achievements and make AI and Machine Learning For Money.
You can learn through E-books.
The best and most traditional way to learn about any field is through books, especially AI and ML.
Many e-books are available such as Artificial Intelligence: A Modern Approach. However, if you want to learn how to create AI, this book is a must-read. The book was written by AI experts Stuart Russell, Peter Norvig. This book covers all aspects of Artificial Intelligence from A to Z, including first-order logic, reinforcement and learning, and neural networks.
Possible with Blogs and Websites
Some many blogs and websites deal with Data Science. Blogs and websites are one of the best learning tools. They also provide many practical skills and experience.
Kdnuggets and Kaggle are some of the most visited blogs and websites. Reddit and Google News on Data Science are two examples of important news sources related to data science.
You can even learn AI and Machine learning through the online platforms
Online courses are the best way to learn AI.
第5步：
通过申请合适的工作获得报酬
开始并保持良好的职业生涯，取得巨大的成就，让人工智能和机器学习有利可图。
你可以通过电子书学习。
了解任何领域的最好和最传统的方法是通过书籍，尤其是人工智能和机器学习。
有许多电子书都不错，比如《人工智能:现代方法》。然而，如果你想学习如何创建人工智能，这本书是必读的。这本书由人工智能专家斯图亚特·罗素(Stuart Russell)、彼得·诺维格(Peter Norvig)撰写。本书涵盖了人工智能从A到Z的各个方面，包括一阶逻辑、强化和学习以及神经网络系统。
可以使用博客和网站
一些博客和网站处理数据科学。博客和网站是最好的学习工具之一。他们还提供了许多实用技能和经验。
Kdnuggets和Kaggle平台是访问量最大的博客和网站。红迪网和谷歌数据科学新闻是与数据科学相关的重要新闻来源的两个例子。
你甚至可以通过在线平台学习人工智能和机器学习
在线课程是学习人工智能的最佳方式。
When you first begin to consider learning machine learning (ML), you should ask yourself a few questions.
What motivates you to pursue it?
Are you a student who has ML in his curriculum? Who aspires to be a data scientist. Are you already working and wish to switch to data science as a career? Are you a business leader who wants to understand ML for their business innovations?
2. How are you going to leverage this knowledge?
Students might use this knowledge to gain a deeper understanding of this area. Already working professionals from different disciplines aspire to add ML to their skill set. Business leaders can leverage this knowledge for the adoption of AI for various business processes.
当你开始考虑学习机器学习（ML）时，你应该问自己几个问题。
是什么激励你去追求它？
你是一个课程体系中有机器学习这门课的学生吗？是渴望成为一名数据科学家？你是否已经在工作并希望转行从事数据科学？你是想了解机器学习的商业创新的商业领袖吗？
2.你将如何利用这些知识？
学生可以利用这些知识来加深对这一领域的理解。来自不同学科的在职专业人士渴望将机器学习添加到他们的技能中。商业领袖可以利用这些知识将人工智能应用于各种商业流程。
There is no predefined skill that one must possess for building an understanding of ML. It is not required of a beginner to know everything right away. You don't need to have any prior programming experience or knowledge of coding. A background in computer science may be beneficial, but it is not an essential need. Anyone who wants to learn Machine Learning can start from the beginning, but it's crucial to know their obxtive.
4. What degree of machine learning knowledge is enough for you?
A student who has an obxtive to be a data scientist must have a fundamental knowledge of machine learning to be part of a data-driven future. Professionals from engineering, data science, IT, or any other discipline have to dig deeper. He must learn mathematics and programming. A business executive who wants to implement machine learning in his company doesn't have to dig too far. Business leaders need machine learning expertise to decide if it can solve a challenge for their firm. So, he must have application-level knowledge.
3.学习机器学习的先决条件是什么水平的技能？
对于建立对机器学习的理解，没有预先定义的技能是必须具备的。初学者不需要马上知道所有事情。您不需要有任何编程经验或编码知识。计算机科学背景可能是有益的，但不是必要的。任何想学习机器学习的人都可以从头开始，但了解他们的目标至关重要。
4.什么程度的机器学习知识对你来说足够？
目标是成为数据科学家的学生必须具备机器学习的基本知识，才能成为数据驱动未来的一部分。来自工程、数据科学、IT或任何其他学科的专业人士都必须深入挖掘。他必须学习数学和编程。想要在公司中实现机器学习的业务主管不必太过深挖。商业领袖需要机器学习专业知识来决定它是否能为他们的公司解决挑战。因此，他必须具备应用程序级的知识。
Working Professionals must have a deep understanding of machine learning. To advance in machine learning, you'll need a diverse range of skills. This is not something that can be done by watching YouTube tutorials. It's more like laying a foundation, and the first step is to get a solid understanding. Begin by taking classes on various online platforms, such as Stanford University's Machine Learning course on Coursera. Otherwise, numerous training institutes provide ML courses.
Students must have a comprehensive understanding. That can be ascertained by attending classroom programs and taking courses that deal with fundamentals.
For business executives, application-level knowledge is sufficient; he does not need to go through complicated structures but only the fundamentals to see how it might benefit his business. He should have comprehensibility where he can leverage the same for innovations. His learning can be accomplished through practical implementations to see how ML can be leveraged for different scenarios. Reading case studies can be of great help.
Disclaimer: I work at Datoin, where one of our core visions is to educate business executives about the relevance of machine learning in today's world and how they can use it to their advantage. Datoin is working on ML-Game, soon to be released, to assist business leaders in comprehending machine learning.
5.选择前进道路：
工作专业人士必须对机器学习有深刻的理解。要在机器学习方面取得进步，你需要多种技能。这不是通过观看油管教程就能做到的。这更像是打下基础，而第一步是获得坚实的理解。首先在各种在线平台上上课，比如斯坦福大学的Coursera平台学习机器学习课程。否则，许多培训机构都会提供机器学习课程。
学生必须有全面的理解。这可以通过参加课堂课程和参加基础课程来确定。
对于业务主管来说，应用程序级知识就足够了；他不需要经历复杂的结构，只需要了解基本原理，就可以看到这对他的业务有什么好处。他应该具有可理解性，以便能够利用这一点进行创新。他的学习可以通过实际实现来完成，以了解如何在不同的场景中利用机器学习。阅读案例研究会有很大帮助。
免责声明：我在Datoin工作，在那里我们的核心愿景之一是教育企业高管，让他们了解机器学习在当今世界的相关性，以及如何利用机器学习为自己创造优势。Datoin正在开发即将发布的机器学习类游戏 ，以帮助商业领袖理解机器学习。
Artificial Intelligence is one breakthrough career path of the twenty-first century. It is a remarkable and intelligent decision to choose AI as your career path. People in the IT industry know its potential and pathbreaking career, which they can achieve through learning AI.
This is the reason many IT people are moving towards learning this technology. Apart from that every single industrial domain such as BFSI, Marketing, HR, manufacturing, etc is getting highly dependent on AI innovations.
AI is not just robots shown in movies. It is much more than just that. It is expert-level coding provided by computer systems through processing models to train them to behave in a certain way.
Anyone who wants to learn AI is a beginner at some time, whether it is a fresh college graduate or an individual already working and wants to move into this career path. Everyone starts somewhere someday. So, never take the pressure of the syllabus. For sure, learning is huge, but it is not complex. If followed properly, anyone can achieve it and interest matters more than the discussion on difficulty level.
Let’s go through the AI learning path and what are the pre-requirements before choosing this technology as your career path. I have covered all the important points below, so do give it a read:
人工智能是21世纪的一条突破性职业道路。选择人工智能作为职业道路是一个非凡而明智的决定。IT行业的人知道其是具备潜力和开创性的职业，他们可以通过学习AI来实现这一目标。
这就是许多IT人员开始学习这项技术的原因。除此之外，每个工业领域，如金融服务和保险业、市场营销、人力资源、制造业等，都高度依赖人工智能方面创新。
人工智能不仅仅是电影中出现的机器人，它的领域远不止于此。它是计算机系统通过处理模型提供的专家级编码，以训练它们以某种方式行为。
任何想要学习AI的人无论是刚毕业的大学毕业生，还是已经开始工作并希望进入这一职业道路的个人，在某种程度上都是初学者。每个人总有一天会从某个地方开始。所以，千万不要承受教学大纲的压力。当然，学习的工作量是巨大的，但并不复杂。如果遵循得当，任何人都可以做到，兴趣比讨论难度更重要。
让我们先了解一下人工智能学习路径，以及选择这项技术作为职业道路之前的先决条件是什么。我下面已经涵盖了所有要点，所以请阅读：
AI is a complicated technology that necessitates a thorough understanding of how systems and software operate. The following are some of the reasons why this technology isn't suitable for fresh graduates:
Compared to a total beginner, a working professional who has accumulated several years of knowledge on how systems work and what approaches are utilised in design thinking will find it easier to understand AI terms.
AI entails deciphering backend data and is based on real-time streaming of data generated by various businesses. In actual life, a fresher will be unable to comprehend the data and its complexities.
This skill is extremely useful for those who have experience with coding. A newbie may know how to code, but there's a good chance he or she will waste a lot of time cleaning up the code in the beginning rather than working on algorithms.
学习人工智能的先决条件：
人工智能是一项复杂的技术，需要彻底了解系统和软件的运行方式。以下是该技术不适合应届毕业生的一些原因：
与一个完全的初学者相比，一个在系统如何工作和设计思维中使用什么方法方面积累了几年知识的专业人士会发现更容易理解AI术语。
人工智能需要破译后端数据，并基于各种业务生成的实时数据流。在实际生活中，初学者将无法理解数据及其复杂性。
对于那些有编码经验的人来说，这项技能非常有用。新手可能知道如何编码，但他或她很有可能会在一开始就浪费大量时间清理代码，而不是研究算法。
AI is in high demand, so it's always a good idea to brush up on your skills. So, for someone who wants to learn AI but doesn't have a clear curriculum, I have written a step-by-step guide to learn AI from basic to intermediate levels.
1. Working on your fundamentals: First and foremost, work on your fundamentals before going on to more complex topics in order to begin working with AI.
2. It's a good idea to start with Maths. Brush up on your math skills and go over the following concepts again:
Matrix and Determinants, as well as Linear Algebra.
Calculus is a branch of mathematics that deals with Differentiation and Integration.
Vectors, statistics and Probability, graph theory.
这主要是因为从事人工智能项目的企业需要一套专门的技能来完成高度复杂、复杂的问题陈述，而很多人都缺乏这种技能。
人工智能的需求量很大，所以提高你的技能总是一个好主意。因此，对于那些想学习人工智能但没有明确课程的人，我写了一份从基础到中级学习人工智能的分步指南。
1.学习基础知识：首先，在学习更复杂的主题之前，先学习基础知识，以便开始使用人工智能。
2.从数学开始是个好主意。复习一下你的数学技能，再复习一下以下概念：
矩阵和行列式，以及线性代数。
微积分是处理微分和积分的数学分支。
向量、统计和概率、图论。
4. Working on Datasets: Once you have mastered any coding language, you can move on to working with backend components such as databases. For example, you may now use SQL connector or other import modules to connect python or frontend IDE.
5. Lastly, I would suggest that you brush up your skills once you're equipped with all the practice work. You should enrol or join in for some courses to speed up the process. There are various online courses to learn from, but I have listed a few good ones which you can undertake for betterment.
There are some amazing institutes from different sites which I surely want to recommend here:
3.编码语言：一旦你掌握了算术技能，你就可以通过选择一种编码语言开始练习编码。可以研究Java或Python。Python是三种语言中最容易学习和练习编码的，因为它有各种各样的程序包，如Numpy和Panda。
4.处理数据集：一旦掌握了任何编码语言，就可以继续处理后端组件，如数据库。例如，您现在可以使用结构化查询语言连接器或其他导入模块连接python或前端成开发环境。
5.最后，我建议你在准备好所有的练习工作后，提高你的技能。你应该注册或参加一些课程以加快进程。有各种各样的在线课程可以学习，但我列出了一些不错的课程，你可以通过它们来提高自己。
我想在这里推荐一些来自不同网站的很棒的学院:
Variable courses that meet your ultimate needs-
Depending on your profile, you can sext from a variety of options. They provide different courses for techies, non-techies, early pros, intermediate, and even leadership level pros. Their courses are associated with elective modules as per candidates domain expertise. Aspirants can choose elective modules and capstone data science projects expertise as per their choice. Available options include people administration, promotion and salesforce assessment, production and telecom, insurance and finance, leisure and travel, transportation, energy, oil and gas, etc.
Additional project expertise and domain-elective study scopes for techies- The advanced AI and ML course imparted by Learnbay has three additional electives apart from the one mentioned earlier. These are from the core engineering field- Advanced data structure and algorithm, system designing, Embedded engineering, etc.
1.Learnbay平台：Learnbay提供基础、中级和高级以及高级管理层的人工智能课程。如果你有一年以上的工作经验，没有其他平台可以像Learnbay一样出色。
满足你最终需求的可变课程-
根据你的个人资料，可以从多种选项中进行选择。他们为技术人员、非技术人员、早期专业人员、中级专业人员甚至领导层专业人员提供不同的课程。根据候选人的专业知识，他们的课程与选修模块相关联。有志者可以根据自己的选择选择选修模块和顶级数据科学项目专业知识。可用的选项包括人事管理、晋升和销售人员评估、生产和电信、保险和金融、休闲和旅游、交通、能源、石油和天然气等。
技术人员的额外项目专业知识和领域选修范围-Learnbay教授的高级人工智能和机器学习课程除了前面提到的课程外，还有三个额外的选修课。这些来自核心工程领域——高级数据结构和算法、系统设计、嵌入式工程等。
The submodules are so well-planned and well-designed that given a bigger find it is easy to learn, although the course covers the most trending and advanced level of industry-specific AI application.
Stacks of the practical assignments with on-time expert feedback-
Starting from the initial programming classes to the advanced ML modelling sessions, you will receive plenty of coding assignments. Experts will provide you with timely feedback on all the submitted assignments so that you can rectify all of your mistakes prior to stepping into the next sessions.
1 to 1 learning support-
Even though you are a beginner, due to extremely personalised teaching, you won't feel the subject hard. You will get proper assistance at every single step of your learning. Apart from that, you get ample flexibility to join the live classes at any time, from any batch.
你从零基础开始学习，直到达到高级水平；
虽然课程涵盖了行业特定人工智能应用的最流行和最先进水平，但子模块的规划和设计非常好，给定一个更大的发现，它很容易学习。
成堆的实践作业和及时的专家反馈
从最初的编程类到高级机器学习建模会话，你将收到大量的编码任务。专家将为你及时反馈所有提交的作业，以便你在进入下一个课程之前纠正所有错误。
1对1学习支持。
尽管你是一名初学者，但由于极其个性化的教学，你不会觉得这门课很难。你在学习的每一步都会得到适当的帮助。除此之外，你还可以在任何时间、任何批次加入实时课堂。
Lifetime access to learning materials-
The recorded copy of attended live classes, all the premium learning materials, remain free to access for the rest of your life.
They also believe that learning should be done through real-world based projects. As a result, you will gain experience with how AI works in 15+ real-time projects.
2. Edureka: Edureka is already a well-known brand that has been on the market for quite some time. They also feature several AI-specific mini-series to learn from. In addition, you will get lifetime access to their courses once you purchase them.
Learbay平台为参加和资格认证黑客马拉松提供指导。这有助于你磨练编码技能。
终身获取学习材料；
参加现场课程的录制课件，所有优质学习材料，在你的余生中都可以免费使用。
他们还认为，学习应该通过基于现实世界的项目来完成。因此，你将获得人工智能如何在15个以上实时项目中工作的经验。
2.Edureka平台：Edureka已经是一个知名品牌，在市场上已经有一段时间了。他们还以几个人工智能特定的迷你系列为特色进行学习。此外，一旦你购买了他们的课程，你就可以终身访问他们的课程。
4. Coursera: On the internet, it is ranked in the top five. More than 3 lakh students have taken Stanford University ML courses, which has received a 4.9 out of 5. It Covers machine learning like Supervised and unsupervised learning, logistic regression and artificial neural networks, ML algo. These are among the topics covered and are best practises in AI and ML
Finally, I would like to recommend Learnbay for working pros and Edureka for freshers. Coursera is suggested exclusively for those who want to learn just for knowing more, not with a career up-gradation target.
3.Simplilearn平台：Simplilearn提供与IBM、AWS、Facebook蓝图和微软的关系，以及一些精彩的课程可供选择。他们非常强调结构化的学习和现实挑战的实践。
4.Coursera平台：在互联网上，它排名前五。超过300万名学生参加了斯坦福大学的机器学习课程，在5门课程中获得了4.9分。该课程涵盖了机器学习，如监督和无监督学习、逻辑回归和人工神经网络、机器学习算法。这些是涵盖的主题之一，是人工智能和机器学习的最佳实践
最后，我想向职业人士推荐Learnbay平台，向初学者推荐Edureka平台。Coursera平台是专为那些只是为了了解更多，而不是为了职业升级的目标的人准备的。