我想學習人工智能和機器學習,我可以從哪里開始呢（一）

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.

機器學習是人工智能的一個非常重要的部分。如果你想學習人工智能，那么機器學習將是學習的一部分。
如圖所示，深度學習和機器學習是人工智能的一部分，如果你想學習人工智能和機器學習，那么你需要這樣開始：
學習編程語言。我建議你學習Python。但是你也可以學習Python和R語言。
然后獲得學習和理解人工智能概念所需的基本數(shù)學和統(tǒng)計學知識。

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.

完成數(shù)學部分后，開始練習機器學習概念。
然后開始學習深度學習，你可以使用TensorFlow計算模型等學習深度學習。
深度學習后，學習自然語言處理、計算機視覺和OpenCV（Intel開源計算機視覺庫）。
如果你想學習人工智能和機器學習，這是你需要遵循的基本結(jié)構(gòu)。在你學習的每一個主題中，嘗試通過解決案例研究進行大量練習，這就是你如何更清楚地理解概念的方法。
對于學習人工智能和機器學習，你應(yīng)該找到合適的課程來接受訓練。我想向在職專業(yè)人士推薦的一個項目是Learnbay提供的人工智能和機器學習認證。該計劃是IBM認證的，該計劃的持續(xù)時間為8個月。

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.

機器學習和人工智能是世界上發(fā)展最快的兩種技術(shù)。隨著各個行業(yè)的進步，這兩個領(lǐng)域迅速發(fā)展起來也就不足為奇了——但這一切的炒作到底是什么？基于項目的學習是當今人工智能流行的主要原因之一。
我們將進一步了解為什么學習機器學習和人工智能如此重要，包括它們在技術(shù)世界之外的一些用途。
以下是學習機器學習和人工智能對你很重要的幾個原因，無論你從事什么工作。
這是支持你事業(yè)的一種方式。隨著你進入更高級的職位，你將使用的技術(shù)將變得更加復雜，復雜的任務(wù)總是通過算法和機器學習得到更好的解決。通過繼續(xù)學習這類技術(shù)，你可以在職業(yè)生涯中承擔這些高級任務(wù)，并證明你有能力。

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.

機器學習有助于它在許多行業(yè)的整合。通過了解機器學習的工作原理，專業(yè)人員更容易將其融入日常生活，并在整個職業(yè)生涯中提高技能。
這不僅對你的事業(yè)有益。機器學習算法可以用來解決我們?nèi)粘Ｉ钪忻媾R的問題。例如，通過機器學習，我們可以創(chuàng)建可集成到城市交通系統(tǒng)中的預測系統(tǒng)，通過更好地規(guī)劃路線來幫助減少交通擁堵并提高安全性。這些都是每天對你、你的家人和你的社區(qū)有益的事情。
如果你了解最新的新聞，你會注意到有很多關(guān)于人工智能也被用于邪惡目的的討論，比如自動武器。
原創(chuàng)翻譯：龍騰網(wǎng) http://www.top-shui.cn 轉(zhuǎn)載請注明出處

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.

現(xiàn)在，如果你想學習人工智能和機器學習，那么這里有一些機會給你!
1.Udemy平臺為初學者提供人工智能和機器學習課程
Udemy平臺的這門在線課程很好地介紹了人工智能和機器學習主題。它是為沒有編程經(jīng)驗的初學者設(shè)計的，同時擁有足夠的材料，人工智能和機器學習方面的狂熱者也可以學習一些新的東西。
2.Coursera平臺關(guān)于不同領(lǐng)域的機器學習課程
該課程由Coursera運營，重點關(guān)注機器學習原理和在特定領(lǐng)域提供最先進結(jié)果的能力。本課程分為五個主要部分：機器學習、統(tǒng)計、優(yōu)化、算法和線性代數(shù)。

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 網(wǎng)上論壇為初學者提供機器學習課程
谷歌機器學習平臺上的課程也更側(cè)重于理論方面，而不是實際方面。它貫穿了最重要的概念，首先簡要介紹了機器學習、人工神經(jīng)網(wǎng)絡(luò)和深度學習，最后用玻爾茲曼機器和自動編碼器完成無監(jiān)督學習。
4.Learnbay平臺的人工智能和機器學習專家課程
Learnbay平臺為所有人工智能和機器學習專家和愛好者提供了最重要的課程之一。如果你想?yún)⒓樱敲聪旅媸悄阈枰私獾谋菊n程的一些必備條件。
從事行業(yè)中的實時項目。習慣與你的導師一起進行解決疑問的交流和互動。此外，你可以接觸到與課程項目相關(guān)的行業(yè)見解，這將相應(yīng)地幫助你。
如果你正在尋找工作機會，這是一門完美的課程。Learnbay平臺讓你有機會獲得幫助，以改進你的職業(yè)生涯，并幫助您參加模擬面試。除此之外，你還可以了解到所有申請最好的跨國公司的方法。數(shù)據(jù)科學課程有退款保證，這意味著在你沒有找到工作之后，你投資的錢將會全部收回。

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:

通過3年靈活付費學習?；旧响`活的付費意味著可以隨時更改批次，可以在付費期間參加多個導師的課程。
Learnbay平臺的另一個好處是有機會接受領(lǐng)域?qū)I(yè)化培訓和課程咨詢。領(lǐng)域?qū)I(yè)化可以幫助你對所有級別進行檢查，如市場營銷、銷售、人力資源，甚至財務(wù)。而且，在你選擇一門課程之前，有機會得到預先咨詢，這能幫助你更好地把握機會。
除非你有實際的項目經(jīng)驗，否則人工智能是不完整的，所以讓我們來看看Learnbay平臺為你帶來了什么：

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.

情緒傳感器：根據(jù)用戶的偏好和情緒檢測情緒和表情符號。
預測需求和銷售：為那些希望得到不同月份銷售和需求預測的大組織工作。
生成語音識別：一個致力于識別和熟悉需要識別的聲音的程序。
分析供應(yīng)鏈：幫助商業(yè)和制造公司在明智選擇的基礎(chǔ)上提出需求和供應(yīng)分析。
Learnbay為您提供了學習人工智能和機器學習專業(yè)知識的巨大機會。一旦你通過課程學習，它將非常適合你的未來。
那么，你認為呢？
如果你想在這個行業(yè)發(fā)展，那么這是你明智選擇的機會！我希望我已經(jīng)很好地回答了你的問題。

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.

你應(yīng)該從自學開始，然后選擇最適合你學習需求的行業(yè)相關(guān)課程。
為準備自學，我建議你遵循以下步驟：
首先，了解人工智能和機器學習的使用時間、地點和方式。對于這樣的理解，最好的方法是
閱讀工業(yè)AI發(fā)展相關(guān)博客。
在你的領(lǐng)域中播放與AI創(chuàng)新相關(guān)的流媒體視頻。
收聽有關(guān)最新人工智能技術(shù)的播客。
關(guān)注不同的與人工智能相關(guān)的社交媒體社區(qū)和頁面，收集關(guān)于人工智能技術(shù)未來趨勢的想法。
通過不同的工作門戶網(wǎng)站，探索您所在領(lǐng)域的人工智能當前就業(yè)市場。

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，開始接觸不同數(shù)據(jù)科學和人工智能課程的學習模塊。通過第一步，你必須收集關(guān)于AI子學習模塊的基本觀點。根據(jù)你的學習模塊研究，選擇最基本的概念，然后進行下一步，初始化人工智能知識的基礎(chǔ)構(gòu)建。
第三，從基本統(tǒng)計開始。根據(jù)你的教育背景，你可以從10+2級高等數(shù)學開始（如果你的數(shù)學知識沒有達到畢業(yè)水平）或回歸分析和推論演算（如果你有畢業(yè)水平/基本統(tǒng)計知識）開始。總體而言，重點關(guān)注以下幾點：
線性代數(shù)，包括向量、矩陣、PCA等。
微積分，包括標量和向量導數(shù)，矩陣微積分等。
概率論包括基本規(guī)則和公理、不同類型的變量等
不需要依賴他人進行與資源相關(guān)的查詢。你會發(fā)現(xiàn)有很多選擇來處理你的主題學習。我個人最喜歡的是可汗學院。
[注：我強烈建議按主題（如矩陣、線性代數(shù)等），而不是按主題（編程、機器學習、統(tǒng)計學等）]

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，開始練習基本編程。最好的策略是將你的學習時間分成兩個相等的部分，學習統(tǒng)計數(shù)據(jù)，并肩學習編程。如果你是一名技術(shù)人員，只需從R編程的基礎(chǔ)數(shù)據(jù)科學開始，或者通過python編程應(yīng)用統(tǒng)計知識。對于非技術(shù)人員，請關(guān)注以下內(nèi)容。
變量、標識符、運算符、操作數(shù)等。
Git源的基本概念。
對于這些，我會向你推薦W3學校，Udemy提供的小型課程，以及Learnbay（Youtube）的免費教程視頻。
開始挖掘機器學習的基本概念。我不會告訴你在這個階段更深入，因為你將通過你選擇的認證課程學習工業(yè)級機器學習概念。然而，為了使你的學習過程更容易，請收集以下主題的想法：

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.

機器學習算法類型
用于分類的機器學習算法。
機器學習模型的類型等。
在這個階段，我將建議Coursera平臺為每個人提供免費的人工智能課程和線分析的免費機器學習課程。
第五步也是最后一步是報名參加人工智能和機器學習課程，該課程提供行業(yè)級學習模塊，涵蓋當前機器學習和人工智能就業(yè)市場的高需求中的所有人工智能和機器學習概念。以下是頂尖的人工智能和機器學習認證課程，提供當前就業(yè)市場勝任的學習模塊。

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平臺的人工智能認證計劃。
Skillslash平臺的全棧的AI和ML程序。
Simplearn平臺的人工智能和機器學習的研究生課程。
Learnbay平臺的機器學習和人工智能高級課程。
所有這些課程都以負擔得起的成本提供適合工作的學習模塊。
【重要提示：一名在職專業(yè)人士在人工智能和機器學習課程上的花費不應(yīng)超過15萬盧比。為了更好地了解這一點，可以查看以下問題：如果一名在職職業(yè)人士在追求數(shù)據(jù)科學或其認證方面投入2-30萬盧比，是否值得？請幫助！】
在上面提到的課程列表中，你可以選擇任何平臺，但我個人推薦的是Learnbay平臺 機器學習和人工智能課程。盡管Learnbay平臺所提到的課程是針對擁有4年以上技術(shù)領(lǐng)域經(jīng)驗的專業(yè)人士的高級課程，但根據(jù)你的工作經(jīng)驗和領(lǐng)域相關(guān)背景，你可以找到另一個合適的替代方案。我更喜歡向有快速職業(yè)轉(zhuǎn)換計劃的機器學習和人工智能抱負者推薦Learnbay平臺，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.

根據(jù)考生的學習需求定制學習模塊和課程。
100%協(xié)助就業(yè)。
綜合數(shù)據(jù)科學項目（你需要一個來獲得人工智能和機器學習工作）直接來自跨國公司和頂尖創(chuàng)業(yè)公司。
全天候技術(shù)支持。
1對1幫助學習，消除疑問。
百分之九十的在線直播互動課。
講師是行業(yè)領(lǐng)導者和印度理工學院 校友。
合理的課程費用不得超過10萬盧比。
甚至是learnbay平臺課程也是非常高效，以至于你可以跳過自學課程的基本統(tǒng)計和編程部分。我希望我的答案有幫助，享受學習。

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.

對于開始人工智能和機器學習，有哪些好的職業(yè)建議？
對于那些對人工智能和機器學習職業(yè)感興趣的人，我們建議如下：
學習如何編碼：編碼是一場令人難以置信的紀律和邏輯練習，當正確的方式完成時，它可以幫助你的大腦掌握你原本不會考慮的問題和解決方案。Python是一種很好的開始方式，它是一種高級而復雜的編程語言，但對機器學習非常實用。
擁有自己的代碼：有些人聲稱自己是機器學習工程師或人工智能工程師，因為他們能夠為特定任務(wù)克隆git源存儲庫（借用某人編寫并公開的代碼塊），或者逐行遵循教程。這是一個很好的開始，然而，對于一家人工智能公司來說，沒有什么比不了解自己在做什么、編碼和部署的工程師更有害的了。理解并擁有你的代碼（盡管你可能認為它很?。⒔o你一個難以置信的優(yōu)勢，并控制你的人工智能項目。如果一開始不是最“優(yōu)化”的代碼，只要你理解它就沒關(guān)系。一個很好的練習是參加Kaggle平臺比賽，或者積極為流行的github（社交編程及代碼托管網(wǎng)站）存儲庫貢獻力量。兩者都將為你提供來自社區(qū)的驗證，這對于雇傭?qū)Ｗ⒂跈C器學習的工程師的公司非常有價值。

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 :)

理解你在編碼什么。機器學習是一個復雜而廣闊的領(lǐng)域，它基于特定的數(shù)學概念和統(tǒng)計方法。了解代碼背后的數(shù)學知識，在優(yōu)化算法、修復錯誤或簡單地識別問題并將其轉(zhuǎn)化為人工智能問題時，會給你帶來難以置信的優(yōu)勢。這也將幫助您收集正確的數(shù)據(jù)集并擁有你的代碼。
不要發(fā)明要解決的問題。創(chuàng)業(yè)公司，尤其是硅谷的創(chuàng)業(yè)公司，因為創(chuàng)始人有一個解決方案（算法、數(shù)據(jù)集、管道等），然后決定發(fā)明一個問題，這并不少見。拜托，別那樣做。成功建立和發(fā)展人工智能初創(chuàng)企業(yè)的最佳方法是識別人們?nèi)粘Ｉ钪械恼鎸崋栴}，然后找到一個你自己編碼、自己理解的解決方案。
最后，人工智能和機器學習是復雜的領(lǐng)域，需要大量的訓練和工作。這是一段漫長的旅程，所以請堅持下去。謙虛但毫不猶豫地提出問題并幫助你的社區(qū)。