我想學(xué)習(xí)人工智能和機(jī)器學(xué)習(xí),我可以從哪里開始呢(四)
I want to learn artificial intelligence and machine learning. Where can I start?譯文簡介
網(wǎng)友:請看,人工智能(AI)是當(dāng)今最受歡迎的技能之一,是一個涵蓋性術(shù)語,包括ML(機(jī)器學(xué)習(xí))和DL(深度學(xué)習(xí))。DL又是ML的一個子集,因此,如果一個人需要掌握AI,首先需要磨練他/她的ML和DL的技能。而且,當(dāng)涉及到自主學(xué)習(xí)機(jī)器學(xué)習(xí)時,決定一個有效的資源是至關(guān)重要的......
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I want to learn artificial intelligence and machine learning. Where can I start?
我想學(xué)習(xí)人工智能和機(jī)器學(xué)習(xí),我可以從哪里開始呢?
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See, AI(Artificial Intelligence) - one of the most sought after skills in today’s time, is an umbrella term and encompasses ML(Machine Learning) and DL(Deep learning). DL again is a subset of ML. Hence, if one needs to master AI, one would first need to hone his/her skills in ML and DL.
And, when it comes to self-learning Machine Learning, it is essential to decide on an effective resource - the one that considers that the students are new to the domain and are not adept with the Machine Learning environment, the one that explains why the program is executing the way it is executing, the one that discusses different approaches the students can use to solve a question and does not restrain their mind to just one method, the one that does not merely glide over the topics. Well, I too learned these lessons the hard way.
But now, having mastered Machine Learning and thence having bagged a high-paying Machine Learning Development job fresh out of college at Airbnb, after facing multiple challenges, I believe, I should put an answer to this question so as to make your learning less troublesome and less time consuming than mine.
請看,人工智能(AI)是當(dāng)今最受歡迎的技能之一,是一個涵蓋性術(shù)語,包括ML(機(jī)器學(xué)習(xí))和DL(深度學(xué)習(xí))。DL又是ML的一個子集,因此,如果一個人需要掌握AI,首先需要磨練他/她的ML和DL的技能。
而且,當(dāng)涉及到自主學(xué)習(xí)機(jī)器學(xué)習(xí)時,決定一個有效的資源是至關(guān)重要的——一個考慮到學(xué)生是新的領(lǐng)域和不熟練的機(jī)器學(xué)習(xí)環(huán)境,一個解釋為什么程序執(zhí)行的方式,它討論了學(xué)生可以用來解決一個問題的不同方法,而不是把他們的思想限制在一種方法上,它不只是跳過主題。好吧,我也經(jīng)歷了慘痛的教訓(xùn)。
但現(xiàn)在,我已經(jīng)掌握了機(jī)器學(xué)習(xí),大學(xué)剛畢業(yè)就在愛彼迎找到了一份高薪的機(jī)器學(xué)習(xí)開發(fā)工作,在經(jīng)歷了多次挑戰(zhàn)之后,我相信我應(yīng)該回答這個問題,讓你的學(xué)習(xí)遇到的麻煩沒我多,花費(fèi)的時間也沒我多。
Moreover, in several resources I found that while solving questions, the author applies a logic/technique that has not been taught to the learner yet. This leads the learner to skip to those sections of the tutorial, where that particular topic is discussed. The concepts taught in those sections in turn apply logic that belongs to another concept. Often, this is a repetitive cycle.
See, as a beginner, much of the learner’s interest in the subject lies in the hands of the tutor and the manner in which the course is delivered. Inefficient coaching can pretty quickly lead to the learner losing interest in the subject, and in worst cases - this can significantly hamper one’s career.
坦率地說,為了掌握機(jī)器學(xué)習(xí),我想盡了一切辦法。我買了太多的課程、書籍和PDF材料,但我總是在學(xué)習(xí)的幾天就碰壁了。在大多數(shù)情況下,我覺得作者/導(dǎo)師急于結(jié)束課程,沒有教育編寫這些代碼背后的基本原理,并假設(shè)自己精通機(jī)器學(xué)習(xí)環(huán)境。但對于初學(xué)者來說,情況并非如此。
此外,在一些資源中,我發(fā)現(xiàn)在解決問題時,作者應(yīng)用了一種尚未教給學(xué)習(xí)者的邏輯/技術(shù)。這導(dǎo)致學(xué)習(xí)者跳到教程中討論特定主題的那些部分。這些章節(jié)中教授的概念依次應(yīng)用屬于另一個概念的邏輯。通常,這是一個重復(fù)的循環(huán)。
作為一名初學(xué)者,學(xué)習(xí)者對這門學(xué)科的興趣很大程度上取決于導(dǎo)師和講授課程的方式。低效的指導(dǎo)會很快導(dǎo)致學(xué)習(xí)者對該學(xué)科失去興趣,在最壞的情況下,這會嚴(yán)重阻礙一個人的職業(yè)生涯。
原創(chuàng)翻譯:龍騰網(wǎng) http://www.top-shui.cn 轉(zhuǎn)載請注明出處
The artificial intelligence (AI) and machine learning (ML) industry is growing by leaps and bounds. The global market size for both these sectors was about $9.51 billion in 2018 and it is expected to rise to $118.6 billion by 2025.
In such a booming environment, if you want to learn AI and ML, you will have to look beyond your immediate obxtives. To begin with, find out the following:
What Is Your Goal?
The first question you need to answer is what precisely your goal is. Your learning process will drastically vary with your answer.
For instance, if you intend to learn AI and ML for getting a job, you'll have to focus on obtaining practical experience. However, if you want to learn them as a part of academics, you'll need to concentrate more on the theoretical aspects.
What's Your Knowledge Level?
Secondly, you will have to figure out how much you already know about AI and ML. Self-learning can work if you possess a basic understanding of:
人工智能(AI)和機(jī)器學(xué)習(xí)(ML)行業(yè)正在飛速發(fā)展。這兩個行業(yè)的全球市場規(guī)模在2018年約為95.1億美元,預(yù)計(jì)到2025年將增至1186億美元。
在這樣一個蓬勃發(fā)展的環(huán)境中,如果你想學(xué)習(xí)AI和ML,你必須超越眼前的目標(biāo)。首先,找出以下內(nèi)容:
你的目標(biāo)是什么?
你需要回答的第一個問題是你的目標(biāo)到底是什么。你的學(xué)習(xí)過程會因你的答案而大不相同。
例如,如果你想學(xué)習(xí)AI和ML以獲得工作,你必須專注于獲得實(shí)踐經(jīng)驗(yàn)。然而,如果你想把它們作為學(xué)術(shù)的一部分來學(xué)習(xí),你需要更多地關(guān)注理論方面。
你的知識水平如何?
其次,你必須弄清楚你對人工智能和機(jī)器學(xué)習(xí)已經(jīng)了解了多少。如果你對以下方面有基本的了解,那么自我學(xué)習(xí)是可行的:
Markup languages.
Foundational mathematics.
Numerical computation.
Multi-variable calculus.
Linear algebra.
Probability theory.
Do You Have Time?
Last but not the least, ascertain the exact amount of time that you have in hand. Irrespective of your goals, knowledge, or time, upGrad can be a great place to start learning about AI and ML. With its specifically designed programs, one-on-one mentorship, and extensive knowledge base, you can learn AI and ML in as few as 11 months!
編程:尤其是Python。
標(biāo)記語言。
基礎(chǔ)數(shù)學(xué)。
數(shù)值計(jì)算。
多變量微積分。
線性代數(shù)。
概率論。
你有時間嗎?
最后但并非最不重要的是,確定你手頭的確切時間。無論你的目標(biāo)、知識或時間如何,upGrad平臺都是開始學(xué)習(xí)AI和ML的好地方。憑借其專門設(shè)計(jì)的課程、一對一的指導(dǎo)和廣泛的知識庫,你可以在短短11個月內(nèi)學(xué)習(xí)AI與ML!
How do I start learning machine learning if I know programming?
Build something; understand the theory behind what you’ve built; repeat.
A lot of people suggest picking up a book like Elements of Statistical Learning and just cranking through that. That might work and is probably the most efficient way of picking up the basics, but it’s kind of a boring path.
Instead, pick a dataset that you’re interested in. Don’t spend time collecting data: it just has to be something that’s easily available, e.g. elections, sports, etc. Identify a prediction problem related to that dataset and try to build a predictive model using libraries like scikit-learn, statsmodels, etc. or their equivalents in R.
Your models’ predictions will probably be pretty bad at first and you’ll find yourself just cycling through different models in order to get better metrics. Resist this temptation. Instead, just pick up a few simple models (e.g. a linear model, maybe an SVM) and then read up on the relevant details from a textbook (e.g. Elements of Statistical Learning or Pattern Recognition and Machine Learning). Of course, if you don’t have the math or CS background needed to understand a particular concept, you should take the time to learn that as well.
Once you feel like you’ve developed a reasonably deep understanding of what you’re doing, attack the same or a different problem again.
If you learn things this way, it’s much harder to forget the details since you’ll end up using a lot of them immediately. Secondly, the entire process will be much more pleasant and interesting than trying to force-feed yourself an entire book with no clear goal in sight.
如果我 懂編程,我如何開始學(xué)習(xí)機(jī)器學(xué)習(xí)?
構(gòu)建一些東西;理解你所構(gòu)建的理論,再重復(fù)
很多人建議讀一本《統(tǒng)計(jì)學(xué)習(xí)要素》之類的書,然后仔細(xì)閱讀。這可能會起作用,可能是最有效的學(xué)習(xí)基礎(chǔ)知識的方法,但這是一條枯燥的道路。
相反,選擇你感興趣的數(shù)據(jù)集。不要花時間收集數(shù)據(jù):它必須是容易獲得的東西,例如選舉、體育等。確定與該數(shù)據(jù)集相關(guān)的預(yù)測問題,并嘗試使用scikit learn、statsmodels等庫或R中的等效庫構(gòu)建預(yù)測模型。
你的模型的預(yù)測起初可能會非常糟糕,你會發(fā)現(xiàn)自己只是在不同的模型中循環(huán),以獲得更好的指標(biāo)。抵制這種誘惑。相反,只需選擇一些簡單的模型(例如線性模型,可能是SVM),然后閱讀教科書中的相關(guān)細(xì)節(jié)(例如統(tǒng)計(jì)學(xué)習(xí)的元素或模式識別和機(jī)器學(xué)習(xí))。當(dāng)然,如果你沒有理解一個特定概念所需的數(shù)學(xué)或計(jì)算機(jī)科學(xué)背景,你也應(yīng)該花時間去學(xué)習(xí)。
一旦你覺得自己對自己正在做的事情有了相當(dāng)深刻的理解,就可以再次解決相同或不同的問題。
如果你用這種方式學(xué)習(xí),那么忘記細(xì)節(jié)就更難了,因?yàn)槟阕罱K會立即使用很多細(xì)節(jié)。其次,整個過程將比試圖在看不到明確目標(biāo)的情況下強(qiáng)迫自己吃透一整本書更愉快和有趣。
How can I start programming machine learning and artificial intelligence?
I’m in no way qualified to fully answer this question - I just asked a very similar question myself a few days ago.
However, taking into account that you are just starting your CS degree, I might have tip or two for you. While what I’m about to say is currently related to AI/ML, you can consider it general advice.
Have you ever experienced what I call “maths enlightenment”? When a concept that felt totally useless finally made sense? I’ve been there several times before: every now and then you realize that after all, derivatives, integrals or differential calculus might not be total bullshit. Or, taking a step back, do you remember when you first realized in practice why it made sense to get yourself to understand multiplication? That you finally knew how many ice creams you can buy on your pocket money?
Machine learning is the first area where after a very long while I felt like I should’ve paid more attention when learning about matrices, differential algebra or any of the “hard” courses, in general. While some of the most practical courses might seem more appealing (I remember being super excited about “iOS development”), and certainly they can be more useful in the very short run, the hard subjects (physics, algorithms, calculus, signals and systems) will pay off forever.
I am saying this as a CS dropout: while you might feel well-prepared to use the technologies and the tools available today, every now and then a new set of technologies pop up where you will sort of be required to know about the low-level stuff in order to understand the bigger picture. That’s where I am today: learning some advanced math that I previously neglected again, finally being able to appreciate some concepts .
我如何開始編寫機(jī)器學(xué)習(xí)和人工智能程序?
我沒有資格完全回答這個問題——幾天前我自己也問了一個非常類似的問題。
然而,考慮到你剛剛開始你的計(jì)算機(jī)科學(xué)學(xué)位,我可能有一兩個建議給你。雖然我要說的是目前與人工智能和機(jī)器學(xué)習(xí)相關(guān)的內(nèi)容,但你可以將其視為一般性建議。
你有沒有經(jīng)歷過我所說的“數(shù)學(xué)啟蒙”?當(dāng)一個感覺完全無用的概念終于有了意義?我以前經(jīng)歷過好幾次:時不時你會意識到,畢竟,導(dǎo)數(shù)、積分或微分學(xué)可能都不是廢話?;蛘撸艘徊?,你還記得當(dāng)你在實(shí)踐中第一次意識到為什么理解乘法是有意義的嗎?你終于知道你可以用零花錢買多少冰淇淋了?
機(jī)器學(xué)習(xí)是第一個領(lǐng)域,在很長一段時間后,我覺得在學(xué)習(xí)矩陣、微分代數(shù)或任何“核心”課程時,我應(yīng)該多加注意。雖然一些最實(shí)用的課程看起來更吸引人(我記得我對“iOS開發(fā)”非常興奮),而且在很短的時間內(nèi)肯定會更有用,但難學(xué)的科目(物理、算法、微積分、信號和系統(tǒng))將永遠(yuǎn)有回報。
我說這句話是因?yàn)橐粋€計(jì)算機(jī)科學(xué)方面輟學(xué)者:雖然你可能已經(jīng)準(zhǔn)備好使用當(dāng)今可用的技術(shù)和工具,每隔一段時間,就會出現(xiàn)一套新的技術(shù),要求你了解底層的東西,了解事物的全貌。這就是我現(xiàn)在的處境:重新學(xué)習(xí)一些以前被我忽視的高等數(shù)學(xué),終于能夠欣賞一些概念。
These last several years have seen remarkable growth for AI. Already, artificial intelligence (AI) and Machine Learning is producing billions of dollars in revenue across a variety of businesses, as well as offering up a plethora of job opportunities.
This trend in artificial intelligence will continue, as the majority of industry verticals embrace the promise of AI to create better tomorrow while also opening up a multitude of employment opportunities. These cutting-edge technologies are available to future professionals, allowing them to establish a long, rewarding, and satisfying career.
Similarly, for true machine learning to work, the computer must be able to identify patterns without being explicitly taught how to do so. Using artificial intelligence, robots may learn a task via experience without being specifically programmed for it.
過去幾年,人工智能取得了顯著的增長。人工智能(AI)和機(jī)器學(xué)習(xí)已經(jīng)在各種業(yè)務(wù)中創(chuàng)造了數(shù)十億美元的收入,并提供了大量的就業(yè)機(jī)會。
人工智能的這一趨勢將繼續(xù)下去,因?yàn)榇蠖鄶?shù)垂直行業(yè)都信奉人工智能的承諾:創(chuàng)造更好的明天,同時也創(chuàng)造了大量的就業(yè)機(jī)會。這些尖端技術(shù)可供未來的專業(yè)人士使用,使他們能夠建立一個長期的、有回報的、令人滿意的職業(yè)生涯。
類似地,要想讓真正的機(jī)器學(xué)習(xí)起作用,計(jì)算機(jī)必須能夠識別模式,而不需要明確地教授如何這樣做。使用人工智能,機(jī)器人可以通過經(jīng)驗(yàn)學(xué)習(xí)任務(wù),而無需專門為其編程。
An approximate road map to becoming proficient in Machine Learning and Artificial Intelligence may be found here. So hold on as I guide you.
Understand the pre-requisites - To begin, you must understand the prerequisites. Most people need to first master Linear Algebra, Multivariate Calculus, Statistics and Python before they can dive into ML and AI like a prodigy. A basic understanding of these topics is sufficient to get started, although a PhD in these fields is not required. For Machine Learning, Linear Algebra, as well as Multivariate Calculus, are both necessary skills. However, the extent to which you need them depends on your role as a data scientist. Around 80% of your work as an ML professional will be spent acquiring and cleaning data. Data collection, analysis, and presentation are all aspects of statistics. This means you'll need to study it, which shouldn't be a surprise to you. There's also one thing you can't ignore - Python.
理解機(jī)器學(xué)習(xí)和人工智能并不是一件容易的事情。首先向計(jì)算機(jī)提供一組高質(zhì)量的數(shù)據(jù),然后使用基于數(shù)據(jù)的各種機(jī)器學(xué)習(xí)模型和已經(jīng)開發(fā)的許多方法對計(jì)算機(jī)進(jìn)行訓(xùn)練。因此,你使用的算法將取決于你正在自動化的數(shù)據(jù)類型和任務(wù)類型。
這里可以找到精通機(jī)器學(xué)習(xí)和人工智能的大致路線圖。所以,在我引導(dǎo)你的時候,堅(jiān)持住。
了解先決條件-首先,你必須了解先決要求。大多數(shù)人需要先掌握線性代數(shù)、多元微積分、統(tǒng)計(jì)學(xué)和Python語言,然后才能像神童一樣潛入人工智能和機(jī)器學(xué)習(xí)。盡管不需要這些領(lǐng)域的博士學(xué)位,但對這些主題做到基本了解就足以開始了。對于機(jī)器學(xué)習(xí),線性代數(shù)和多元微積分都是必要的技能。然而,你需要它們的程度取決于你作為數(shù)據(jù)科學(xué)家的角色。作為機(jī)器學(xué)習(xí)專業(yè)人員,你大約80%的工作將用于獲取和清理數(shù)據(jù)。數(shù)據(jù)收集、分析和呈現(xiàn)都是統(tǒng)計(jì)的各個方面。這意味著你需要研究它,這對你來說并不奇怪。還有一件事你不能忽視——Python語言。
Take part in projects - The fascinating and entertaining part of Machine Learning comes after understanding the foundations. PROJECTS! Through the integration of your largely theoretical understanding with real-world application, you will be able to improve your machine learning skills. To get you started, the 'Titanic: Machine Learning from Disaster project,' the 'Digit Recognizer project,' etc. can help you gain confidence. It is also possible to use the projects as a tool while looking for employment in this sector.
A smart option when you've learned the basics is to network with other data scientists or enrol in professional training courses. Furthermore, courses like as upGrad, Skillslash, Edureka are some of the top AI and ML platforms on the market right now. There are a number of reasons why Skillslash, for example, provides some of the best intermediate-level AI and ML courses.
熟悉不同的機(jī)器語言和人工智能思想——模型、特征(標(biāo)簽)、目標(biāo)(標(biāo)簽),訓(xùn)練和預(yù)測是其中幾個基本概念。機(jī)器學(xué)習(xí)包括有監(jiān)督、無監(jiān)督、半監(jiān)督和強(qiáng)化學(xué)習(xí)。為了在機(jī)器學(xué)習(xí)中使用數(shù)據(jù),需要花費(fèi)大量精力來收集、整合、清理和準(zhǔn)備數(shù)據(jù)。你需要高質(zhì)量的數(shù)據(jù),但隨機(jī)生成大量數(shù)據(jù)并不少見。其他要求包括研究替代模型和使用真實(shí)世界數(shù)據(jù)。這可以幫助你了解在特定情況下什么樣的模型是合適的。還有學(xué)習(xí)使用各種模型分析數(shù)據(jù)的正確方法的問題。了解各種型號上使用的各種調(diào)整設(shè)置和調(diào)整方法可以使這更容易實(shí)現(xiàn)。
參與項(xiàng)目-機(jī)器學(xué)習(xí)的迷人和有趣的部分是在了解基礎(chǔ)之后參與項(xiàng)目!通過將你的基本理論理解與實(shí)際應(yīng)用相結(jié)合,你將能夠提高你的機(jī)器學(xué)習(xí)技能。為了讓你開始,“泰坦尼克號:災(zāi)難中的機(jī)器學(xué)習(xí)項(xiàng)目”、“數(shù)字識別器項(xiàng)目”等可以幫助你獲得信心。你也可以將這些項(xiàng)目作為在該行業(yè)尋找就業(yè)機(jī)會的工具。
當(dāng)你學(xué)會了基礎(chǔ)知識后,一個明智的選擇是與其他數(shù)據(jù)科學(xué)家建立網(wǎng)絡(luò)或參加專業(yè)培訓(xùn)課程。此外,upGrad、Skillslash、Edureka等課程是目前市場上一些頂級的人工智能和機(jī)器學(xué)習(xí)平臺。例如,Skillslash提供一些最好的中級的人工智能和機(jī)器學(xué)習(xí)課程是有很多原因的。
Individually tailored courses are available through Skillslash for a moderate fee.
Over the course of the nine-month term, students who sign up for Skillslash get access to 350+ hours of live sessions.
In addition, they give students the chance to collaborate on real-world projects companies. For students to enhance their portfolios and receive a project experience certificate, they can engage in joint projects with businesses.
Courses, such as Data Science and AI Program, Full-Stack AI and ML Program, are available to students from a range of professional backgrounds.
In order to keep expenses down, they've created affordable courses that costs Rs. 89,000 and Rs. 35,000 for professionals and newcomers respectively.
Final Thoughts - Self-driving cars, chatbots, realistic speech recognition, efficient internet search, and a vastly improved understanding of the human genome have all been made possible by artificial intelligence and machine learning in recent years. Most of us utilise AI and ML hundreds of times a day, without ever recognising it. That's why all learners receive intermediate-level tuition from Skillslash.
一位行業(yè)專家審查了他們的項(xiàng)目經(jīng)驗(yàn),這在該領(lǐng)域非常重要。
通過Skillslash提供個性化定制課程,收費(fèi)適中。
在九個月的學(xué)期中,報名參加Skillslash的學(xué)生可以獲得350多個小時的直播課程。
此外,他們還為學(xué)生提供了在現(xiàn)實(shí)世界項(xiàng)目公司中合作的機(jī)會。為了提高學(xué)生的投資組合并獲得項(xiàng)目經(jīng)驗(yàn)證書,他們可以與企業(yè)聯(lián)合開展項(xiàng)目。
課程,如數(shù)據(jù)科學(xué)和人工智能計(jì)劃、全棧人工智能和ML計(jì)劃,可供各種專業(yè)背景的學(xué)生學(xué)習(xí)。
為了降低開支,他們?yōu)閷I(yè)人士和新人開設(shè)了價格合理的課程,分別為89000盧比和35000盧比。
最后的想法-近年來,人工智能和機(jī)器學(xué)習(xí)使自動駕駛汽車、聊天機(jī)器人、逼真的語音識別、高效的互聯(lián)網(wǎng)搜索以及對人類基因組的理解大大提高成為可能。我們中的大多數(shù)人每天使用AI和ML數(shù)百次,但從未意識到這一點(diǎn)。這就是為什么所有學(xué)習(xí)者都會從Skillslash接受中級水平的學(xué)費(fèi)的原因。
One of the most common questions in today’s day and age is “How to start learning Artificial Intelligence and Machine Learning?”. This is not an easy question to answer. One needs to go through various online e Books, websites, and blogs, courses, classroom training, training institutes, and on-the-job training to learn this. This answer, however, leads to a range of other questions that need to be answered, such as which book to buy or which online course to take.
In order to begin, here is a list of details of the sources mentioned above:
E-books: Reading a book or two related to a field is one of the best and simplest ways to learn about anything. Some of the e-books available for data science can be read for a good start. Some of them can be downloaded for free in order to get a good understanding of data science. E-books offer the unique advantage of allowing a person to gain knowledge at their own pace without relying on other individuals. As a bonus, it's one of the best and most affordable ways to learn about Data Science, as well as in-depth. However, e-books are primarily hindered by a lack of support. The issue is accentuated if the person does not have any prior background in the field. Furthermore, it is difficult to learn from books due to a lack of practical experience and knowledge, which is a major drawback in a field such as this.
當(dāng)今時代最常見的問題之一是“如何開始學(xué)習(xí)人工智能和機(jī)器學(xué)習(xí)?”。這不是一個容易回答的問題。人們需要通過各種在線電子書、網(wǎng)站、博客、課程、課堂培訓(xùn)、培訓(xùn)機(jī)構(gòu)和在職培訓(xùn)來了解這一點(diǎn)。然而,這個答案引出了一系列其他需要回答的問題,比如買哪本書或參加哪門在線課程。
首先,這里列出了上述來源的詳細(xì)信息:
電子書:閱讀一兩本與某一領(lǐng)域相關(guān)的書是了解任何事物的最好和最簡單的方法之一。一些可用于數(shù)據(jù)科學(xué)的電子書可以作為一個良好的開端。其中一些可以免費(fèi)下載,以便更好地了解數(shù)據(jù)科學(xué)。電子書具有獨(dú)特的優(yōu)勢,可以讓一個人以自己的速度獲得知識,而不依賴其他人。作為獎勵,它是學(xué)習(xí)數(shù)據(jù)科學(xué)以及深入學(xué)習(xí)的最好和最實(shí)惠的方法之一。然而,電子書主要受到缺乏支持的阻礙。如果此人沒有該領(lǐng)域的任何背景,這個問題就會更加突出。此外,由于缺乏實(shí)踐經(jīng)驗(yàn)和知識,很難從書中學(xué)習(xí),這是此類領(lǐng)域的一個主要缺陷。
原創(chuàng)翻譯:龍騰網(wǎng) http://www.top-shui.cn 轉(zhuǎn)載請注明出處
For data science: Python Data Science Handbook by Jake VanderPlas
For Machine Learning: Understanding Machine Learning by Shai Shalev-Shwartz and Shai Ben-David
For Artificial Intelligence or Deep Learning: Deep Learning by Ian Goodfellow
Websites and blogs: An ocean of data science-related websites and blogs exist today, which leaves many asking, Which is the best place to learn machine learning and AI? Learning can be found primarily through websites and blogs, which often offer useful practical knowledge. The most popular websites and blogs are - Kdnuggets, Kaggle, Data Camp, etc. Additionally, Reddit's and Google Newsblogs also provide information on Data Science, which is a key component to the dissemination of news on Data Science. Moreover, the comments given by users on these blogs help elaborate certain concepts. Similar to learning from books, they are not dynamic and do not offer individual support.
Online courses: Based on my 5+ years of experience in this field, enrolling yourself in an online training program is the best way to build your future in the field of Artificial Intelligence and Machine Learning. When you are guided by professionals and industry experts, your chances of success increase. Many amazing platforms exist that teach machine learning concepts, so you should definitely use them. This again raises questions such as – Which course should I take to learn AI? You can find many e-learning courses on the internet provided by excellent online course providers. A few of them are listed below:
你可以參考的書籍列表:
對于數(shù)據(jù)科學(xué):Jake VanderPlas的Python數(shù)據(jù)科學(xué)手冊
《機(jī)器學(xué)習(xí):理解機(jī)器學(xué)習(xí)》作者:Shai Shalev Shwartz和Shai Ben David
人工智能或深度學(xué)習(xí):伊恩·古德費(fèi)羅的深度學(xué)習(xí)
網(wǎng)站和博客:如今存在著大量與數(shù)據(jù)科學(xué)相關(guān)的網(wǎng)站和博客,這讓許多人不禁要問,哪一個地方是學(xué)習(xí)機(jī)器學(xué)習(xí)和人工智能的最佳場所?學(xué)習(xí)主要可以通過網(wǎng)站和博客找到,這些網(wǎng)站和博客通常提供有用的實(shí)用知識。最受歡迎的網(wǎng)站和博客是-Kdnuggets、Kaggle、Data Camp等。此外,Reddit和谷歌新聞博客還提供數(shù)據(jù)科學(xué)信息,這是數(shù)據(jù)科學(xué)新聞傳播的關(guān)鍵組成部分。此外,用戶在這些博客上的評論有助于闡述某些概念。與從書本上學(xué)習(xí)類似,它們不是動態(tài)的,也不提供個人支持。
在線課程:基于我在該領(lǐng)域5年以上的經(jīng)驗(yàn),注冊在線培訓(xùn)計(jì)劃是在人工智能和機(jī)器學(xué)習(xí)領(lǐng)域構(gòu)建未來的最佳方式。當(dāng)你在專業(yè)人士和行業(yè)專家的指導(dǎo)下,你成功的機(jī)會會增加。存在著許多教授機(jī)器學(xué)習(xí)概念的令人驚嘆的平臺,因此你絕對應(yīng)該使用它們。這再次引發(fā)了一些問題,比如——我應(yīng)該選擇哪門課程來學(xué)習(xí)人工智能?你可以在互聯(lián)網(wǎng)上找到許多優(yōu)秀在線課程提供商提供的在線學(xué)習(xí)課程。以下列出了其中一些:
Stanford University – Machine Learning – The course is available on Coursera. This course is taught by Google Brain founder Andrew Ng. In this course, you can choose whether to take it for free or to pay in order to get a certificate that will aid you in your career in the future. You will learn about examples of AI-driven technologies from real life, like advanced mechanisms of web search and speech recognition. You will also learn how neural networks learn.
In addition to this, there is one specific site I would like to suggest, which is Skillslash. Definitely one of the most effective ways to learn about artificial intelligence, data science, and machine learning. When I came across this platform during my research, I was impressed by the features offered here.
與谷歌AI一起學(xué)習(xí)——它由谷歌推出,旨在向公眾傳達(dá)什么是人工智能以及它是如何工作的。盡管該資源仍處于起步階段,但它已經(jīng)提供了一個機(jī)器學(xué)習(xí)課程,其中包含了谷歌的TensorFlow庫。它介紹了TensorFlow并解釋了神經(jīng)網(wǎng)絡(luò)是如何設(shè)計(jì)的,從初學(xué)者到專家,每個人都會發(fā)現(xiàn)它很有用。
斯坦福大學(xué)-機(jī)器學(xué)習(xí)-該課程可在Coursera平臺上可獲得。本課程由Google Brain創(chuàng)始人吳恩達(dá)教授。在這門課程中,你可以選擇是免費(fèi)還是付費(fèi),以獲得一份有助于你未來職業(yè)生涯的證書。逆將了解現(xiàn)實(shí)生活中人工智能驅(qū)動技術(shù)的例子,如網(wǎng)絡(luò)搜索和語音識別的高級機(jī)制。你還將學(xué)習(xí)神經(jīng)網(wǎng)絡(luò)系統(tǒng)是如何學(xué)習(xí)的。
除此之外,我還想推薦一個特定的網(wǎng)站,那就是Skillslash。絕對是學(xué)習(xí)人工智能、數(shù)據(jù)科學(xué)和機(jī)器學(xué)習(xí)最有效的方法之一。當(dāng)我在研究期間遇到這個平臺時,我對這里提供的功能印象深刻。