我想学习人工智能和机器学习,我可以从哪里开始呢（一）
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?
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?
评论翻译
Sakshi Sharma
Machine Learning is a very important part of Artificial Intelligence. If you are looking to learn AI then Machine Learning will be a part of the learning.
As you can see in the image, Deep Learning and Machine Learning is a part of Artificial Intelligence and if you want to learn Artificial Intelligence and Machine Learning then this is how you need to start:
Learn a Programming language. I would suggest you learn Python. But you can learn Python and R as well.
Then gain some knowledge in the basic mathematics and statistics that are needed for learning and understanding the concepts in AI.
After you are done with the mathematical part, start practicing the machine learning concepts.
机器学习是人工智能的一个非常重要的部分。如果你想学习人工智能，那么机器学习将是学习的一部分。
如图所示，深度学习和机器学习是人工智能的一部分，如果你想学习人工智能和机器学习，那么你需要这样开始：
学习编程语言。我建议你学习Python。但是你也可以学习Python和R语言。
然后获得学习和理解人工智能概念所需的基本数学和统计学知识。
原创翻译：龙腾网 https://www.ltaaa.cn 转载请注明出处
Machine Learning is a very important part of Artificial Intelligence. If you are looking to learn AI then Machine Learning will be a part of the learning.
As you can see in the image, Deep Learning and Machine Learning is a part of Artificial Intelligence and if you want to learn Artificial Intelligence and Machine Learning then this is how you need to start:
Learn a Programming language. I would suggest you learn Python. But you can learn Python and R as well.
Then gain some knowledge in the basic mathematics and statistics that are needed for learning and understanding the concepts in AI.
After you are done with the mathematical part, start practicing the machine learning concepts.
机器学习是人工智能的一个非常重要的部分。如果你想学习人工智能，那么机器学习将是学习的一部分。
如图所示，深度学习和机器学习是人工智能的一部分，如果你想学习人工智能和机器学习，那么你需要这样开始：
学习编程语言。我建议你学习Python。但是你也可以学习Python和R语言。
然后获得学习和理解人工智能概念所需的基本数学和统计学知识。
原创翻译：龙腾网 https://www.ltaaa.cn 转载请注明出处
Then start learning Deep Learning and you can learn Deep learning using tensor flow etc.
After Deep Learning, learn NLP, Computer Vision, and OpenCV.
This is a basic structure that you need to follow if you want to learn Artificial Intelligence and Machine Learning. In each of the topics that you learn try to practice a lot by solving case studies, that is how you will get to understand the concepts more clearly.
For learning AI and ML, you should find the right course wherein you can get trained. One program that I would like to recommend for working professionals is the AI and ML Certification offered by Learnbay. This program is IBM Certified and the duration of this program is 8 months.
完成数学部分后，开始练习机器学习概念。
然后开始学习深度学习，你可以使用TensorFlow计算模型等学习深度学习。
深度学习后，学习自然语言处理、计算机视觉和OpenCV（Intel开源计算机视觉库）。
如果你想学习人工智能和机器学习，这是你需要遵循的基本结构。在你学习的每一个主题中，尝试通过解决案例研究进行大量练习，这就是你如何更清楚地理解概念的方法。
对于学习人工智能和机器学习，你应该找到合适的课程来接受训练。我想向在职专业人士推荐的一个项目是Learnbay提供的人工智能和机器学习认证。该计划是IBM认证的，该计划的持续时间为8个月。
After Deep Learning, learn NLP, Computer Vision, and OpenCV.
This is a basic structure that you need to follow if you want to learn Artificial Intelligence and Machine Learning. In each of the topics that you learn try to practice a lot by solving case studies, that is how you will get to understand the concepts more clearly.
For learning AI and ML, you should find the right course wherein you can get trained. One program that I would like to recommend for working professionals is the AI and ML Certification offered by Learnbay. This program is IBM Certified and the duration of this program is 8 months.
完成数学部分后，开始练习机器学习概念。
然后开始学习深度学习，你可以使用TensorFlow计算模型等学习深度学习。
深度学习后，学习自然语言处理、计算机视觉和OpenCV（Intel开源计算机视觉库）。
如果你想学习人工智能和机器学习，这是你需要遵循的基本结构。在你学习的每一个主题中，尝试通过解决案例研究进行大量练习，这就是你如何更清楚地理解概念的方法。
对于学习人工智能和机器学习，你应该找到合适的课程来接受训练。我想向在职专业人士推荐的一个项目是Learnbay提供的人工智能和机器学习认证。该计划是IBM认证的，该计划的持续时间为8个月。
Shweta Tiwari
Machine Learning and Artificial Intelligence are two of the fastest-growing technologies in the world. With advancements being made in every industry, it's no wonder that these two fields have taken off - but what is all this hype really about? Project based learning is one of the main thing why AI is trending these days.
We'll take a closer look at why learning Machine Learning and AI is so important, including some of their uses outside of the technology world.
Here are a few reasons why learning Machine Learning and AI is important to you, no matter what your job.
It's a way to support your career. As you move into more senior roles, the technology you'll use will become more complex, and complex tasks are always better solved through algorithms and machine learning. By continuing to learn about this type of technology, you can take on these advanced tasks in your career and prove that you're capable.
机器学习和人工智能是世界上发展最快的两种技术。随着各个行业的进步，这两个领域迅速发展起来也就不足为奇了——但这一切的炒作到底是什么？基于项目的学习是当今人工智能流行的主要原因之一。
我们将进一步了解为什么学习机器学习和人工智能如此重要，包括它们在技术世界之外的一些用途。
以下是学习机器学习和人工智能对你很重要的几个原因，无论你从事什么工作。
这是支持你事业的一种方式。随着你进入更高级的职位，你将使用的技术将变得更加复杂，复杂的任务总是通过算法和机器学习得到更好的解决。通过继续学习这类技术，你可以在职业生涯中承担这些高级任务，并证明你有能力。
Machine Learning and Artificial Intelligence are two of the fastest-growing technologies in the world. With advancements being made in every industry, it's no wonder that these two fields have taken off - but what is all this hype really about? Project based learning is one of the main thing why AI is trending these days.
We'll take a closer look at why learning Machine Learning and AI is so important, including some of their uses outside of the technology world.
Here are a few reasons why learning Machine Learning and AI is important to you, no matter what your job.
It's a way to support your career. As you move into more senior roles, the technology you'll use will become more complex, and complex tasks are always better solved through algorithms and machine learning. By continuing to learn about this type of technology, you can take on these advanced tasks in your career and prove that you're capable.
机器学习和人工智能是世界上发展最快的两种技术。随着各个行业的进步，这两个领域迅速发展起来也就不足为奇了——但这一切的炒作到底是什么？基于项目的学习是当今人工智能流行的主要原因之一。
我们将进一步了解为什么学习机器学习和人工智能如此重要，包括它们在技术世界之外的一些用途。
以下是学习机器学习和人工智能对你很重要的几个原因，无论你从事什么工作。
这是支持你事业的一种方式。随着你进入更高级的职位，你将使用的技术将变得更加复杂，复杂的任务总是通过算法和机器学习得到更好的解决。通过继续学习这类技术，你可以在职业生涯中承担这些高级任务，并证明你有能力。
Machine Learning is beneficial for its integration throughout many industries. By understanding how Machine Learning works, it becomes easier for professionals to integrate it into their daily lives - as well as improve their skills throughout their careers.
It's not just beneficial for your career. Machine learning algorithms can be used to solve issues we face in our everyday lives. For example, with machine learning, we can create predictive systems that can be integrated into city transportation systems, to help reduce traffic congestion and improve safety by better planning routes. These are the types of things that will benefit you, your family, and your community daily.
If you stay up-to-date with the news, you'll have noticed that there's a lot of talk about AI being used for evil purposes as well - such as autonomous weapons.
机器学习有助于它在许多行业的整合。通过了解机器学习的工作原理，专业人员更容易将其融入日常生活，并在整个职业生涯中提高技能。
这不仅对你的事业有益。机器学习算法可以用来解决我们日常生活中面临的问题。例如，通过机器学习，我们可以创建可集成到城市交通系统中的预测系统，通过更好地规划路线来帮助减少交通拥堵并提高安全性。这些都是每天对你、你的家人和你的社区有益的事情。
如果你了解最新的新闻，你会注意到有很多关于人工智能也被用于邪恶目的的讨论，比如自动武器。
It's not just beneficial for your career. Machine learning algorithms can be used to solve issues we face in our everyday lives. For example, with machine learning, we can create predictive systems that can be integrated into city transportation systems, to help reduce traffic congestion and improve safety by better planning routes. These are the types of things that will benefit you, your family, and your community daily.
If you stay up-to-date with the news, you'll have noticed that there's a lot of talk about AI being used for evil purposes as well - such as autonomous weapons.
机器学习有助于它在许多行业的整合。通过了解机器学习的工作原理，专业人员更容易将其融入日常生活，并在整个职业生涯中提高技能。
这不仅对你的事业有益。机器学习算法可以用来解决我们日常生活中面临的问题。例如，通过机器学习，我们可以创建可集成到城市交通系统中的预测系统，通过更好地规划路线来帮助减少交通拥堵并提高安全性。这些都是每天对你、你的家人和你的社区有益的事情。
如果你了解最新的新闻，你会注意到有很多关于人工智能也被用于邪恶目的的讨论，比如自动武器。
Now if you wish to learn AI and ML, then here are some opportunities for you!
1. Udemy's AI and ML Course for Beginners
This online course by Udemy gives a good introduction to artificial intelligence and machine learning topics. It is designed for beginners, who have no experience in programming, while simultaneously having enough material that AI and machine learning fanatics can also learn something new.
2. Coursera's ML course on different fields
The course is run by Coursera and focuses on Machine Learning principles and the ability to deliver state-of-the-art results in a specific area. The course is divided into five main parts: machine learning, statistics, optimization, algorithms, and linear algebra.
现在，如果你想学习人工智能和机器学习，那么这里有一些机会给你!
1.Udemy平台为初学者提供人工智能和机器学习课程
Udemy平台的这门在线课程很好地介绍了人工智能和机器学习主题。它是为没有编程经验的初学者设计的，同时拥有足够的材料，人工智能和机器学习方面的狂热者也可以学习一些新的东西。
2.Coursera平台关于不同领域的机器学习课程
该课程由Coursera运营，重点关注机器学习原理和在特定领域提供最先进结果的能力。本课程分为五个主要部分：机器学习、统计、优化、算法和线性代数。
1. Udemy's AI and ML Course for Beginners
This online course by Udemy gives a good introduction to artificial intelligence and machine learning topics. It is designed for beginners, who have no experience in programming, while simultaneously having enough material that AI and machine learning fanatics can also learn something new.
2. Coursera's ML course on different fields
The course is run by Coursera and focuses on Machine Learning principles and the ability to deliver state-of-the-art results in a specific area. The course is divided into five main parts: machine learning, statistics, optimization, algorithms, and linear algebra.
现在，如果你想学习人工智能和机器学习，那么这里有一些机会给你!
1.Udemy平台为初学者提供人工智能和机器学习课程
Udemy平台的这门在线课程很好地介绍了人工智能和机器学习主题。它是为没有编程经验的初学者设计的，同时拥有足够的材料，人工智能和机器学习方面的狂热者也可以学习一些新的东西。
2.Coursera平台关于不同领域的机器学习课程
该课程由Coursera运营，重点关注机器学习原理和在特定领域提供最先进结果的能力。本课程分为五个主要部分：机器学习、统计、优化、算法和线性代数。
3. Online Google's ML Course for Beginners
The course on Google's machine learning platform also concentrates more on theoretical aspects than practical ones. It runs through the most important concepts, starting with a short introduction to machine learning, neural networks, and deep learning, finishing with unsupervised learning with Boltzmann machines and autoencoders.
4. Learnbay’s AI and ML Experts Course
Learnbay provides one of the most important courses for all ML and AI Experts and enthusiasts. If you wish to participate, then here are some of the requisites for this course you need to take a look at.
Work on live projects from the industry. Get accustomed to living and interactive sessions with your mentors for doubt solving sessions. Also, you can be exposed to industry insights with capstone projects that are going to help you accordingly.
It is a perfect course if you are looking out for a job opportunity. Learnbay gives you the chance to be assisted to revamp your career and help you attend Mock Interviews. Apart from that, you can get a check on all the ways to apply at the best MNCs. The Data Science course comes at a money back guarantee which means that you will get all your money back which you invested, right after you don’t secure a job.
3.Google 网上论坛为初学者提供机器学习课程
谷歌机器学习平台上的课程也更侧重于理论方面，而不是实际方面。它贯穿了最重要的概念，首先简要介绍了机器学习、人工神经网络和深度学习，最后用玻尔兹曼机器和自动编码器完成无监督学习。
4.Learnbay平台的人工智能和机器学习专家课程
Learnbay平台为所有人工智能和机器学习专家和爱好者提供了最重要的课程之一。如果你想参加，那么下面是你需要了解的本课程的一些必备条件。
从事行业中的实时项目。习惯与你的导师一起进行解决疑问的交流和互动。此外，你可以接触到与课程项目相关的行业见解，这将相应地帮助你。
如果你正在寻找工作机会，这是一门完美的课程。Learnbay平台让你有机会获得帮助，以改进你的职业生涯，并帮助您参加模拟面试。除此之外，你还可以了解到所有申请最好的跨国公司的方法。数据科学课程有退款保证，这意味着在你没有找到工作之后，你投资的钱将会全部收回。
The course on Google's machine learning platform also concentrates more on theoretical aspects than practical ones. It runs through the most important concepts, starting with a short introduction to machine learning, neural networks, and deep learning, finishing with unsupervised learning with Boltzmann machines and autoencoders.
4. Learnbay’s AI and ML Experts Course
Learnbay provides one of the most important courses for all ML and AI Experts and enthusiasts. If you wish to participate, then here are some of the requisites for this course you need to take a look at.
Work on live projects from the industry. Get accustomed to living and interactive sessions with your mentors for doubt solving sessions. Also, you can be exposed to industry insights with capstone projects that are going to help you accordingly.
It is a perfect course if you are looking out for a job opportunity. Learnbay gives you the chance to be assisted to revamp your career and help you attend Mock Interviews. Apart from that, you can get a check on all the ways to apply at the best MNCs. The Data Science course comes at a money back guarantee which means that you will get all your money back which you invested, right after you don’t secure a job.
3.Google 网上论坛为初学者提供机器学习课程
谷歌机器学习平台上的课程也更侧重于理论方面，而不是实际方面。它贯穿了最重要的概念，首先简要介绍了机器学习、人工神经网络和深度学习，最后用玻尔兹曼机器和自动编码器完成无监督学习。
4.Learnbay平台的人工智能和机器学习专家课程
Learnbay平台为所有人工智能和机器学习专家和爱好者提供了最重要的课程之一。如果你想参加，那么下面是你需要了解的本课程的一些必备条件。
从事行业中的实时项目。习惯与你的导师一起进行解决疑问的交流和互动。此外，你可以接触到与课程项目相关的行业见解，这将相应地帮助你。
如果你正在寻找工作机会，这是一门完美的课程。Learnbay平台让你有机会获得帮助，以改进你的职业生涯，并帮助您参加模拟面试。除此之外，你还可以了解到所有申请最好的跨国公司的方法。数据科学课程有退款保证，这意味着在你没有找到工作之后，你投资的钱将会全部收回。
Learn with a 3-year flexible subscxtion. Basically flexible subscxtion means in which one can change batches anytime he/she wants, can attend classes from multiple instructors during the subscxtion period.
Another good thing about Learnbay is the chance to have domain specialization training and course counseling. Domain specialization helps you to keep a check on all levels such as Marketing, Sales, HR, and even Finance. And, get the chance to be pre-counseled before you choose a course which will help you to manage your opportunities much better.
AI is not complete until and unless you have hands-on experience doing projects so let’s take a look at what Learnbay brings for you:
通过3年灵活付费学习。基本上灵活的付费意味着可以随时更改批次，可以在付费期间参加多个导师的课程。
Learnbay平台的另一个好处是有机会接受领域专业化培训和课程咨询。领域专业化可以帮助你对所有级别进行检查，如市场营销、销售、人力资源，甚至财务。而且，在你选择一门课程之前，有机会得到预先咨询，这能帮助你更好地把握机会。
除非你有实际的项目经验，否则人工智能是不完整的，所以让我们来看看Learnbay平台为你带来了什么：
原创翻译：龙腾网 https://www.ltaaa.cn 转载请注明出处
Another good thing about Learnbay is the chance to have domain specialization training and course counseling. Domain specialization helps you to keep a check on all levels such as Marketing, Sales, HR, and even Finance. And, get the chance to be pre-counseled before you choose a course which will help you to manage your opportunities much better.
AI is not complete until and unless you have hands-on experience doing projects so let’s take a look at what Learnbay brings for you:
通过3年灵活付费学习。基本上灵活的付费意味着可以随时更改批次，可以在付费期间参加多个导师的课程。
Learnbay平台的另一个好处是有机会接受领域专业化培训和课程咨询。领域专业化可以帮助你对所有级别进行检查，如市场营销、销售、人力资源，甚至财务。而且，在你选择一门课程之前，有机会得到预先咨询，这能帮助你更好地把握机会。
除非你有实际的项目经验，否则人工智能是不完整的，所以让我们来看看Learnbay平台为你带来了什么：
原创翻译：龙腾网 https://www.ltaaa.cn 转载请注明出处
Emotions Sensor: Detects emotions and emoticons based on the users preference and mood.
Forecasting Demand and Sales: Works for big organizations who are looking forward to get their sales and demands forecasted for different months.
Generating Voice Recognition: A program which works on recognizing and famializing voices to be identified.
Supply chain for Analytics: Helps business and manufacturing companies to come up with demand and supply analytics on smart-choice basis.
Learnbay gives you a huge opportunity to learn the specialties of ML and AI. Once you learn through the course, it will be a perfect fit for your future.
So, what do you think?
If you are looking to grow in this sector then here’s your chance to choose wisely! I hope I have answered your question well.
情绪传感器：根据用户的偏好和情绪检测情绪和表情符号。
预测需求和销售：为那些希望得到不同月份销售和需求预测的大组织工作。
生成语音识别：一个致力于识别和熟悉需要识别的声音的程序。
分析供应链：帮助商业和制造公司在明智选择的基础上提出需求和供应分析。
Learnbay为您提供了学习人工智能和机器学习专业知识的巨大机会。一旦你通过课程学习，它将非常适合你的未来。
那么，你认为呢？
如果你想在这个行业发展，那么这是你明智选择的机会！我希望我已经很好地回答了你的问题。
Forecasting Demand and Sales: Works for big organizations who are looking forward to get their sales and demands forecasted for different months.
Generating Voice Recognition: A program which works on recognizing and famializing voices to be identified.
Supply chain for Analytics: Helps business and manufacturing companies to come up with demand and supply analytics on smart-choice basis.
Learnbay gives you a huge opportunity to learn the specialties of ML and AI. Once you learn through the course, it will be a perfect fit for your future.
So, what do you think?
If you are looking to grow in this sector then here’s your chance to choose wisely! I hope I have answered your question well.
情绪传感器：根据用户的偏好和情绪检测情绪和表情符号。
预测需求和销售：为那些希望得到不同月份销售和需求预测的大组织工作。
生成语音识别：一个致力于识别和熟悉需要识别的声音的程序。
分析供应链：帮助商业和制造公司在明智选择的基础上提出需求和供应分析。
Learnbay为您提供了学习人工智能和机器学习专业知识的巨大机会。一旦你通过课程学习，它将非常适合你的未来。
那么，你认为呢？
如果你想在这个行业发展，那么这是你明智选择的机会！我希望我已经很好地回答了你的问题。
Raj Khurana
You should start with self-learning, followed by choosing an industry-relevant course that best fits your learning needs.
For preparatory self-study, I would suggest you follow the below steps:
First, understand the when, where, and how artificial intelligence and machine learning is getting used. For such understanding, the best way is to start
Reading industrial AI development related blogs.
Streaming videos related to AI innovation in your domain.
Listen to podcasts about the latest AI technologies.
Follow different social media communities and pages related to artificial intelligence to gather ideas about upcoming trends in AI technologies.
Explore the current job market of AI in your domain through different job portals.
你应该从自学开始，然后选择最适合你学习需求的行业相关课程。
为准备自学，我建议你遵循以下步骤：
首先，了解人工智能和机器学习的使用时间、地点和方式。对于这样的理解，最好的方法是
阅读工业AI发展相关博客。
在你的领域中播放与AI创新相关的流媒体视频。
收听有关最新人工智能技术的播客。
关注不同的与人工智能相关的社交媒体社区和页面，收集关于人工智能技术未来趋势的想法。
通过不同的工作门户网站，探索您所在领域的人工智能当前就业市场。
You should start with self-learning, followed by choosing an industry-relevant course that best fits your learning needs.
For preparatory self-study, I would suggest you follow the below steps:
First, understand the when, where, and how artificial intelligence and machine learning is getting used. For such understanding, the best way is to start
Reading industrial AI development related blogs.
Streaming videos related to AI innovation in your domain.
Listen to podcasts about the latest AI technologies.
Follow different social media communities and pages related to artificial intelligence to gather ideas about upcoming trends in AI technologies.
Explore the current job market of AI in your domain through different job portals.
你应该从自学开始，然后选择最适合你学习需求的行业相关课程。
为准备自学，我建议你遵循以下步骤：
首先，了解人工智能和机器学习的使用时间、地点和方式。对于这样的理解，最好的方法是
阅读工业AI发展相关博客。
在你的领域中播放与AI创新相关的流媒体视频。
收听有关最新人工智能技术的播客。
关注不同的与人工智能相关的社交媒体社区和页面，收集关于人工智能技术未来趋势的想法。
通过不同的工作门户网站，探索您所在领域的人工智能当前就业市场。
Second, start reaching out about the learning modules of different data science and AI courses. Already, through the first step, you have to gather basic ideas about AI sub-learning modules. According to your learning modules research, choose the most fundamental concepts and proceed to the next step of initialising the foundational building of AI knowledge.
Third, start with basic statistics. Depending on your educational background, you can start either with 10+2 level advanced mathematics (if you don't hold graduation level math knowledge) or regression and inferential calculus (in case you have graduation level/basic statistical knowledge.) Overall, focus on the following:
Linear algebra, including vectors, matrix, PCA, etc.
Calculus including scaler and vector derivative, matrix calculus, etc.
Probability including basic rules and axioms, different types of variables, etc.
No need to depend on another person for resource-related queries. You will find plenty of options to process your topic-wise learning. My personal favourite is Khan Academy.
[Note: I highly recommend going by topics (like matrix, linear algebra, etc.) instead of the subject (programming, ML, statistics... like this) ]
其次，开始接触不同数据科学和人工智能课程的学习模块。通过第一步，你必须收集关于AI子学习模块的基本观点。根据你的学习模块研究，选择最基本的概念，然后进行下一步，初始化人工智能知识的基础构建。
第三，从基本统计开始。根据你的教育背景，你可以从10+2级高等数学开始（如果你的数学知识没有达到毕业水平）或回归分析和推论演算（如果你有毕业水平/基本统计知识）开始。总体而言，重点关注以下几点：
线性代数，包括向量、矩阵、PCA等。
微积分，包括标量和向量导数，矩阵微积分等。
概率论包括基本规则和公理、不同类型的变量等
不需要依赖他人进行与资源相关的查询。你会发现有很多选择来处理你的主题学习。我个人最喜欢的是可汗学院。
[注：我强烈建议按主题（如矩阵、线性代数等），而不是按主题（编程、机器学习、统计学等）]
Third, start with basic statistics. Depending on your educational background, you can start either with 10+2 level advanced mathematics (if you don't hold graduation level math knowledge) or regression and inferential calculus (in case you have graduation level/basic statistical knowledge.) Overall, focus on the following:
Linear algebra, including vectors, matrix, PCA, etc.
Calculus including scaler and vector derivative, matrix calculus, etc.
Probability including basic rules and axioms, different types of variables, etc.
No need to depend on another person for resource-related queries. You will find plenty of options to process your topic-wise learning. My personal favourite is Khan Academy.
[Note: I highly recommend going by topics (like matrix, linear algebra, etc.) instead of the subject (programming, ML, statistics... like this) ]
其次，开始接触不同数据科学和人工智能课程的学习模块。通过第一步，你必须收集关于AI子学习模块的基本观点。根据你的学习模块研究，选择最基本的概念，然后进行下一步，初始化人工智能知识的基础构建。
第三，从基本统计开始。根据你的教育背景，你可以从10+2级高等数学开始（如果你的数学知识没有达到毕业水平）或回归分析和推论演算（如果你有毕业水平/基本统计知识）开始。总体而言，重点关注以下几点：
线性代数，包括向量、矩阵、PCA等。
微积分，包括标量和向量导数，矩阵微积分等。
概率论包括基本规则和公理、不同类型的变量等
不需要依赖他人进行与资源相关的查询。你会发现有很多选择来处理你的主题学习。我个人最喜欢的是可汗学院。
[注：我强烈建议按主题（如矩阵、线性代数等），而不是按主题（编程、机器学习、统计学等）]
Fourth, start practising basic programming. The best strategy is to divide your learning time into two equal parts, learn the stats, and, side-by-side, keep learning to program. If you are a techie, simply start with basic data science with R programming or applying statistical knowledge through python programming. For non-techies, concentrate on the following.
Variable, identifier, operators, operands, etc.
Basic concepts of Git sources.
For these, I would recommend you the W3 school, and small courses offered by Udemy, and the free tutorial videos of Learnbay (Youtube).
start digging into the basic concepts of machine learning. I will not tell you to dive deeper at this stage because you will learn the industry-grade machine learning concept through your chosen certification course. However, just to make your learning process easier, gather an idea about the following topics:
第四，开始练习基本编程。最好的策略是将你的学习时间分成两个相等的部分，学习统计数据，并肩学习编程。如果你是一名技术人员，只需从R编程的基础数据科学开始，或者通过python编程应用统计知识。对于非技术人员，请关注以下内容。
变量、标识符、运算符、操作数等。
Git源的基本概念。
对于这些，我会向你推荐W3学校，Udemy提供的小型课程，以及Learnbay（Youtube）的免费教程视频。
开始挖掘机器学习的基本概念。我不会告诉你在这个阶段更深入，因为你将通过你选择的认证课程学习工业级机器学习概念。然而，为了使你的学习过程更容易，请收集以下主题的想法：
原创翻译：龙腾网 https://www.ltaaa.cn 转载请注明出处
Variable, identifier, operators, operands, etc.
Basic concepts of Git sources.
For these, I would recommend you the W3 school, and small courses offered by Udemy, and the free tutorial videos of Learnbay (Youtube).
start digging into the basic concepts of machine learning. I will not tell you to dive deeper at this stage because you will learn the industry-grade machine learning concept through your chosen certification course. However, just to make your learning process easier, gather an idea about the following topics:
第四，开始练习基本编程。最好的策略是将你的学习时间分成两个相等的部分，学习统计数据，并肩学习编程。如果你是一名技术人员，只需从R编程的基础数据科学开始，或者通过python编程应用统计知识。对于非技术人员，请关注以下内容。
变量、标识符、运算符、操作数等。
Git源的基本概念。
对于这些，我会向你推荐W3学校，Udemy提供的小型课程，以及Learnbay（Youtube）的免费教程视频。
开始挖掘机器学习的基本概念。我不会告诉你在这个阶段更深入，因为你将通过你选择的认证课程学习工业级机器学习概念。然而，为了使你的学习过程更容易，请收集以下主题的想法：
原创翻译：龙腾网 https://www.ltaaa.cn 转载请注明出处
Machine learning algorithm types
Machine learning algorithm for classification.
Types of machine learning models etc.
For this stage, I’ll suggest the free AI for everyone course by Coursera and the free Machine learning course by EdX.
The fifth and final step is to enrol for an artificial intelligence and machine learning course that offers an industry-grade learning module and covers all the AI and ML concepts in the high demand of the present machine learning and artificial intelligence job market. Below are the top AI and ML certification courses offering present job-market competent learning modules.
机器学习算法类型
用于分类的机器学习算法。
机器学习模型的类型等。
在这个阶段，我将建议Coursera平台为每个人提供免费的人工智能课程和线分析的免费机器学习课程。
第五步也是最后一步是报名参加人工智能和机器学习课程，该课程提供行业级学习模块，涵盖当前机器学习和人工智能就业市场的高需求中的所有人工智能和机器学习概念。以下是顶尖的人工智能和机器学习认证课程，提供当前就业市场胜任的学习模块。
Machine learning algorithm for classification.
Types of machine learning models etc.
For this stage, I’ll suggest the free AI for everyone course by Coursera and the free Machine learning course by EdX.
The fifth and final step is to enrol for an artificial intelligence and machine learning course that offers an industry-grade learning module and covers all the AI and ML concepts in the high demand of the present machine learning and artificial intelligence job market. Below are the top AI and ML certification courses offering present job-market competent learning modules.
机器学习算法类型
用于分类的机器学习算法。
机器学习模型的类型等。
在这个阶段，我将建议Coursera平台为每个人提供免费的人工智能课程和线分析的免费机器学习课程。
第五步也是最后一步是报名参加人工智能和机器学习课程，该课程提供行业级学习模块，涵盖当前机器学习和人工智能就业市场的高需求中的所有人工智能和机器学习概念。以下是顶尖的人工智能和机器学习认证课程，提供当前就业市场胜任的学习模块。
Certification program on AI by Coursera.
Fullstack AI and ML Program by Skillslash.
Post Graduate Program in AI and ML by Simplilearn.
Advanced Program in AI and ML by Learnbay.
All of these courses offer job-competent learning modules at affordable costs.
[ Important Note: A working professional should not spend more than 1.5 lakhs for AI and ML courses. To get a better idea in this regard, you can check the following question: Is it worth it if a working professional invests 2-3 lac on pursuing Data Science or its certification? Please help! ]
Of the list mentioned above of courses you can choose from anyone, but my personal recommendation goes with the Learnbay AI and ML course. Although the mentioned courses by Learnbay are advanced for professionals with 4+ years of tech domain, you can find another suitable alternative according to your work experience and domain-related background. I prefer recommending Learnbay to AI and ML aspirants with fast career switch plans because of the following reasons:
Coursera平台的人工智能认证计划。
Skillslash平台的全栈的AI和ML程序。
Simplearn平台的人工智能和机器学习的研究生课程。
Learnbay平台的机器学习和人工智能高级课程。
所有这些课程都以负担得起的成本提供适合工作的学习模块。
【重要提示：一名在职专业人士在人工智能和机器学习课程上的花费不应超过15万卢比。为了更好地了解这一点，可以查看以下问题：如果一名在职职业人士在追求数据科学或其认证方面投入2-30万卢比，是否值得？请帮助！】
在上面提到的课程列表中，你可以选择任何平台，但我个人推荐的是Learnbay平台 机器学习和人工智能课程。尽管Learnbay平台所提到的课程是针对拥有4年以上技术领域经验的专业人士的高级课程，但根据你的工作经验和领域相关背景，你可以找到另一个合适的替代方案。我更喜欢向有快速职业转换计划的机器学习和人工智能抱负者推荐Learnbay平台，原因如下：
Fullstack AI and ML Program by Skillslash.
Post Graduate Program in AI and ML by Simplilearn.
Advanced Program in AI and ML by Learnbay.
All of these courses offer job-competent learning modules at affordable costs.
[ Important Note: A working professional should not spend more than 1.5 lakhs for AI and ML courses. To get a better idea in this regard, you can check the following question: Is it worth it if a working professional invests 2-3 lac on pursuing Data Science or its certification? Please help! ]
Of the list mentioned above of courses you can choose from anyone, but my personal recommendation goes with the Learnbay AI and ML course. Although the mentioned courses by Learnbay are advanced for professionals with 4+ years of tech domain, you can find another suitable alternative according to your work experience and domain-related background. I prefer recommending Learnbay to AI and ML aspirants with fast career switch plans because of the following reasons:
Coursera平台的人工智能认证计划。
Skillslash平台的全栈的AI和ML程序。
Simplearn平台的人工智能和机器学习的研究生课程。
Learnbay平台的机器学习和人工智能高级课程。
所有这些课程都以负担得起的成本提供适合工作的学习模块。
【重要提示：一名在职专业人士在人工智能和机器学习课程上的花费不应超过15万卢比。为了更好地了解这一点，可以查看以下问题：如果一名在职职业人士在追求数据科学或其认证方面投入2-30万卢比，是否值得？请帮助！】
在上面提到的课程列表中，你可以选择任何平台，但我个人推荐的是Learnbay平台 机器学习和人工智能课程。尽管Learnbay平台所提到的课程是针对拥有4年以上技术领域经验的专业人士的高级课程，但根据你的工作经验和领域相关背景，你可以找到另一个合适的替代方案。我更喜欢向有快速职业转换计划的机器学习和人工智能抱负者推荐Learnbay平台，原因如下：
Customised learning modules and courses according to candidates' learning needs.
100% placement assistance.
Integrated data science projects (you need one to get ML and AI jobs) directly from MNCs and top-notch startups.
Round the clock tech support.
1 to 1 learning assistance, doubt clearing.
Cent per cent live interactive online classes.
Instructors are industry leads and IIT alumni.
Reasonable course fees that never exceed 1 lakh INR.
Even the learnbay course is so efficient that you can skip the self-study session's basic statistics and programming part. I hope my answer helps. Happy learning.
根据考生的学习需求定制学习模块和课程。
100%协助就业。
综合数据科学项目（你需要一个来获得人工智能和机器学习工作）直接来自跨国公司和顶尖创业公司。
全天候技术支持。
1对1帮助学习，消除疑问。
百分之九十的在线直播互动课。
讲师是行业领导者和印度理工学院 校友。
合理的课程费用不得超过10万卢比。
甚至是learnbay平台课程也是非常高效，以至于你可以跳过自学课程的基本统计和编程部分。我希望我的答案有帮助，享受学习。
原创翻译：龙腾网 https://www.ltaaa.cn 转载请注明出处
100% placement assistance.
Integrated data science projects (you need one to get ML and AI jobs) directly from MNCs and top-notch startups.
Round the clock tech support.
1 to 1 learning assistance, doubt clearing.
Cent per cent live interactive online classes.
Instructors are industry leads and IIT alumni.
Reasonable course fees that never exceed 1 lakh INR.
Even the learnbay course is so efficient that you can skip the self-study session's basic statistics and programming part. I hope my answer helps. Happy learning.
根据考生的学习需求定制学习模块和课程。
100%协助就业。
综合数据科学项目（你需要一个来获得人工智能和机器学习工作）直接来自跨国公司和顶尖创业公司。
全天候技术支持。
1对1帮助学习，消除疑问。
百分之九十的在线直播互动课。
讲师是行业领导者和印度理工学院 校友。
合理的课程费用不得超过10万卢比。
甚至是learnbay平台课程也是非常高效，以至于你可以跳过自学课程的基本统计和编程部分。我希望我的答案有帮助，享受学习。
原创翻译：龙腾网 https://www.ltaaa.cn 转载请注明出处
Quora Session with Co-founders of Crossing Minds, Inc. ·
What are some good career tips for getting started in AI and machine learning?
For those of you who are interested in careers in AI and machine learning, we recommend the following:
Learn how to CODE: Coding is an incredible exercise of discipline and logic, which - when done the right way - can help your mind grasp problems and solutions you wouldn’t have originally considered. A great (way) to start would be Python, which is a high-level and sophisticated programming language, yet very practical for machine learning.
OWN what you’re coding: Some people claim to be ML engineers or AI engineers because they’re capable cloning a git repository (borrowing a chunk of code that someone wrote and made public) for a specific task or follow a tutorial line-by-line. It is a great start, however, there’s nothing more harmful (technically speaking) for an AI company than an engineer that does not understand what (s)he is doing, coding and deploying. Understanding and owning your code (as small as you may think it is) will give you an incredible advantage and control over your AI project. It doesn’t matter if it is not the most “optimized” code at first, as long as you understand it. One good exercise would be to participate to Kaggle competitions or actively contribute to a popular github repository. Both will give you a validation from the community, that are very valuable for companies hiring ML-focused engineers.
对于开始人工智能和机器学习，有哪些好的职业建议？
对于那些对人工智能和机器学习职业感兴趣的人，我们建议如下：
学习如何编码：编码是一场令人难以置信的纪律和逻辑练习，当正确的方式完成时，它可以帮助你的大脑掌握你原本不会考虑的问题和解决方案。Python是一种很好的开始方式，它是一种高级而复杂的编程语言，但对机器学习非常实用。
拥有自己的代码：有些人声称自己是机器学习工程师或人工智能工程师，因为他们能够为特定任务克隆git源存储库（借用某人编写并公开的代码块），或者逐行遵循教程。这是一个很好的开始，然而，对于一家人工智能公司来说，没有什么比不了解自己在做什么、编码和部署的工程师更有害的了。理解并拥有你的代码（尽管你可能认为它很小）将给你一个难以置信的优势，并控制你的人工智能项目。如果一开始不是最“优化”的代码，只要你理解它就没关系。一个很好的练习是参加Kaggle平台比赛，或者积极为流行的github（社交编程及代码托管网站）存储库贡献力量。两者都将为你提供来自社区的验证，这对于雇佣专注于机器学习的工程师的公司非常有价值。
What are some good career tips for getting started in AI and machine learning?
For those of you who are interested in careers in AI and machine learning, we recommend the following:
Learn how to CODE: Coding is an incredible exercise of discipline and logic, which - when done the right way - can help your mind grasp problems and solutions you wouldn’t have originally considered. A great (way) to start would be Python, which is a high-level and sophisticated programming language, yet very practical for machine learning.
OWN what you’re coding: Some people claim to be ML engineers or AI engineers because they’re capable cloning a git repository (borrowing a chunk of code that someone wrote and made public) for a specific task or follow a tutorial line-by-line. It is a great start, however, there’s nothing more harmful (technically speaking) for an AI company than an engineer that does not understand what (s)he is doing, coding and deploying. Understanding and owning your code (as small as you may think it is) will give you an incredible advantage and control over your AI project. It doesn’t matter if it is not the most “optimized” code at first, as long as you understand it. One good exercise would be to participate to Kaggle competitions or actively contribute to a popular github repository. Both will give you a validation from the community, that are very valuable for companies hiring ML-focused engineers.
对于开始人工智能和机器学习，有哪些好的职业建议？
对于那些对人工智能和机器学习职业感兴趣的人，我们建议如下：
学习如何编码：编码是一场令人难以置信的纪律和逻辑练习，当正确的方式完成时，它可以帮助你的大脑掌握你原本不会考虑的问题和解决方案。Python是一种很好的开始方式，它是一种高级而复杂的编程语言，但对机器学习非常实用。
拥有自己的代码：有些人声称自己是机器学习工程师或人工智能工程师，因为他们能够为特定任务克隆git源存储库（借用某人编写并公开的代码块），或者逐行遵循教程。这是一个很好的开始，然而，对于一家人工智能公司来说，没有什么比不了解自己在做什么、编码和部署的工程师更有害的了。理解并拥有你的代码（尽管你可能认为它很小）将给你一个难以置信的优势，并控制你的人工智能项目。如果一开始不是最“优化”的代码，只要你理解它就没关系。一个很好的练习是参加Kaggle平台比赛，或者积极为流行的github（社交编程及代码托管网站）存储库贡献力量。两者都将为你提供来自社区的验证，这对于雇佣专注于机器学习的工程师的公司非常有价值。
UNDERSTAND what you’re coding. Machine Learning is a complex and vast field, which is based on specific mathematical concepts and statistical approaches. Understanding the mathematics behind the code, will give you an incredible advantage when it comes to optimizing your algorithm, fixing a bug, or simply recognizing a problem and translating it into an AI problem. This also will help you gather the right dataset and own your code.
Don’t Invent Problems to Solve. It’s not uncommon to see startups, especially in Silicon Valley, launched because the founders have a solution (an algorithm, a dataset, a pipeline, etc..) and decide to then invent a problem. Please, don’t do that. The best way to successfully build and grow an AI startup is to identify a REAL problem in people's everyday lives and then find a solution that you CODE, OWN, and UNDERSTAND.
Finally, AI and ML are complicated fields that require a lot of discipline and work. This is a long journey, so hold on. Be humble, never hesitate to ask questions and help your community :)
理解你在编码什么。机器学习是一个复杂而广阔的领域，它基于特定的数学概念和统计方法。了解代码背后的数学知识，在优化算法、修复错误或简单地识别问题并将其转化为人工智能问题时，会给你带来难以置信的优势。这也将帮助您收集正确的数据集并拥有你的代码。
不要发明要解决的问题。创业公司，尤其是硅谷的创业公司，因为创始人有一个解决方案（算法、数据集、管道等），然后决定发明一个问题，这并不少见。拜托，别那样做。成功建立和发展人工智能初创企业的最佳方法是识别人们日常生活中的真实问题，然后找到一个你自己编码、自己理解的解决方案。
最后，人工智能和机器学习是复杂的领域，需要大量的训练和工作。这是一段漫长的旅程，所以请坚持下去。谦虚但毫不犹豫地提出问题并帮助你的社区。
Don’t Invent Problems to Solve. It’s not uncommon to see startups, especially in Silicon Valley, launched because the founders have a solution (an algorithm, a dataset, a pipeline, etc..) and decide to then invent a problem. Please, don’t do that. The best way to successfully build and grow an AI startup is to identify a REAL problem in people's everyday lives and then find a solution that you CODE, OWN, and UNDERSTAND.
Finally, AI and ML are complicated fields that require a lot of discipline and work. This is a long journey, so hold on. Be humble, never hesitate to ask questions and help your community :)
理解你在编码什么。机器学习是一个复杂而广阔的领域，它基于特定的数学概念和统计方法。了解代码背后的数学知识，在优化算法、修复错误或简单地识别问题并将其转化为人工智能问题时，会给你带来难以置信的优势。这也将帮助您收集正确的数据集并拥有你的代码。
不要发明要解决的问题。创业公司，尤其是硅谷的创业公司，因为创始人有一个解决方案（算法、数据集、管道等），然后决定发明一个问题，这并不少见。拜托，别那样做。成功建立和发展人工智能初创企业的最佳方法是识别人们日常生活中的真实问题，然后找到一个你自己编码、自己理解的解决方案。
最后，人工智能和机器学习是复杂的领域，需要大量的训练和工作。这是一段漫长的旅程，所以请坚持下去。谦虚但毫不犹豫地提出问题并帮助你的社区。
很赞 2
收藏