機器學習實用指南

by Karlijn Willems

通過Karlijn Willems

機器學習方法：實用指南 (How Machines Learn: A Practical Guide)

You may have heard about machine learning from interesting applications like spam filtering, optical character recognition, and computer vision.

您可能已經(jīng)聽說過從有趣的應用程序(例如垃圾郵件過濾，光學字符識別和計算機視覺)中學習機器的方法。

Getting started with machine learning is long process that involves going through several resources. There are books for newbies, academic papers, guided exercises, and standalone projects. It’s easy to lose track of what you need to learn among all these options.

機器學習入門是一個漫長的過程，涉及到涉及多種資源。 有適合新手的書籍，學術(shù)論文，指導練習和獨立項目。 在所有這些選項中，很容易忘記需要學習的內(nèi)容。

So in today’s post, I’ll list seven steps (and 50+ resources) that can help you get started in this exciting field of Computer Science, and ramp up toward becoming a machine learning hero.

因此，在今天的帖子中，我將列出七個步驟(以及50多個資源)，這些步驟可以幫助您開始在這個令人興奮的計算機科學領(lǐng)域入門，并逐步成為一名機器學習英雄。

Note that this list of resources is not exhaustive and is meant to get you started. There are many more resources around.

請注意，此資源列表并不詳盡，旨在幫助您入門。 周圍還有更多資源。

1.獲得必要的背景知識 (1. Get the necessary background knowledge)

You might remember from DataCamp’s Learn Data Science infographic that mathematics and statistics are key to starting machine learning (ML). The foundations might seem quite easy because it’s just three topics. But don’t forget that these are in fact three broad topics.

您可能從DataCamp的“ 學習數(shù)據(jù)科學”信息圖中還記得，數(shù)學和統(tǒng)計學是啟動機器學習(ML)的關(guān)鍵。 建立基礎似乎很容易，因為這只是三個主題。 但是請不要忘記，這些實際上是三個主要主題。

There are two things that are very important to keep in mind here:

這里有兩件事要記住很重要：

• First, you’ll definitely want some further guidance on what exactly you need to cover to get started.
  首先，您肯定會想要一些入門方面的進一步指導。
• Second, these are the foundations of your further learning. Don’t be scared to take your time. Get the knowledge on which you’ll build everything.
  其次，這些是您進一步學習的基礎。 不要害怕花時間。 獲得構(gòu)建一切所需的知識。

The first point is simple: it’s a good idea to cover linear algebra and statistics. These two are the bare minimum that one should understand. But while you’re at it, you should also try to cover topics such as optimization and advanced calculus. They will come in handy when you’re getting deeper into ML.

第一點很簡單：覆蓋線性代數(shù)和統(tǒng)計量是一個好主意。 這兩個是應該理解的最低要求。 但是當您使用它時，您還應該嘗試涵蓋諸如優(yōu)化和高級演算之類的主題。 當您深入學習ML時，它們將派上用場。

Here are some pointers on where to get started if you are starting from zero:

如果您從零開始，以下是一些入門指南：

• Khan Academy is a good resource for beginners. Consider taking the Linear Algebra and Calculus courses.

  汗學院對于初學者來說是一個很好的資源。 考慮參加線性代數(shù)和微積分課程。

• Go to MIT OpenCourseWare and take the Linear Algebra course.

  轉(zhuǎn)到MIT OpenCourseWare并學習線性代數(shù)課程。

• Take this Coursera course for an introduction to descriptive statistics, probability theory, and inferential statistics.

  參加此Coursera課程以介紹描述性統(tǒng)計，概率論和推論統(tǒng)計。

If you’re more into books, consider the following:

如果您更喜歡書籍，請考慮以下事項：

• Linear Algebra and Its Applications,

  線性代數(shù)及其應用 ，

• Applied Linear Algebra,

  應用線性代數(shù)

• 3,000 Solved Problems in Linear Algebra,

  3,000個線性代數(shù)中的已解決問題 ，

• MIT Online Texbooks

  麻省理工學院在線Texbooks

However, in most cases, you’ll start off already knowing some things about statistics and mathematics. Or maybe you have already gone through all the theory resources listed above.

但是，在大多數(shù)情況下，您將已經(jīng)開始了解一些有關(guān)統(tǒng)計和數(shù)學的知識。 也許您已經(jīng)遍歷了上面列出的所有理論資源。

In these cases, it’s a good idea to recap and assess your knowledge honestly. Are there any areas that you need to revise or are you good for now?

在這種情況下，最好對自己的知識進行回顧和評估。 您是否有需要修改的地方，或者您目前是否擅長？

If you’re all set, it’s time to go ahead and apply all that knowledge with R or Python. As a general guideline, it’s a good idea to pick one and get started with that language. Later, you can still add the other programming language to your skill set.

如果一切都準備好了，該是繼續(xù)使用R或Python應用所有知識的時候了。 作為一般準則，最好選擇一個并開始使用該語言。 以后，您仍然可以將其他編程語言添加到您的技能中。

Why is all this programming knowledge necessary?

為什么所有這些編程知識都是必需的？

Well, you’ll see that the courses listed above (or those you have taken in school or university) will provide you with a more theoretical (and not applied) introduction to mathematics and statistics topics. However, ML is very applied and you’ll need to be able to apply all the topics you have learned. So it’s a good idea to go over the materials again, but this time in an applied way.

好吧，您會看到上面列出的課程(或您在學校或大學里修過的課程)將為您提供關(guān)于數(shù)學和統(tǒng)計學主題的更理論性(而非實際應用)的介紹。 但是，ML的應用非常廣泛，您需要能夠應用所學的所有主題。 因此，再次遍歷這些材料是一個好主意，但是這次以一種實用的方式進行。

If you want to master the basics of R and Python, consider the following courses:

如果您想掌握R和Python的基礎知識，請考慮以下課程：

• DataCamp’s introductory Python or R courses: Intro to Python for Data Science or Introduction to R Programming.

  DataCamp的Python或R入門課程： 數(shù)據(jù)科學Python 入門或R編程簡介 。

• Introductory Python and R courses from Edx: Introduction to Python for Data Science and Introduction to R for Data Science.

  Edx的Python和R入門課程： Python for Data Science 入門和R for Data Science入門 。

• There are many other free courses out there. Check out Coursera or Codeacademy for more.

  那里還有許多其他免費課程。 進一步了解Coursera或Codeacademy 。

When you have nailed down the basics, check out DataCamp’s blog on the 40+ Python Statistics For Data Science Resources. This post offers 40+ resources on the statistics topics you need to know to get started with data science (and by extension also ML).

掌握了基礎知識之后，請訪問DataCamp的博客，該博客上有40多個用于數(shù)據(jù)科學資源的Python Statistics 。 這篇文章提供了40多個關(guān)于統(tǒng)計主題的資源，您需要了解這些知識才能著手進行數(shù)據(jù)科學(并擴展為ML)。

Also make sure you check out this SciPy tutorial on vectors and arrays and this workshop on Scientific Computing with Python.

另外，還要確保您查看了有關(guān)向量和數(shù)組的SciPy教程以及有關(guān)使用Python進行科學計算的研討會 。

To get hands-on with Python and calculus, you can check out the SymPy package.

要動手使用Python和演算，您可以查看SymPy軟件包 。

2.不要害怕投資機器學習的“理論” (2. Don’t be scared to invest in the “theory” of ML)

A lot of people don’t make the effort to go through some more theoretical material because it’s “dry” or “boring.” But going through the theory and really investing your time in it is essential and invaluable in the long run. You’ll better understand new advancements in machine learning, and you’ll be able to link back to your background knowledge. This will help you stay motivated.

許多人不愿意嘗試一些更理論的材料，因為它是“枯燥的”或“無聊的”。 但是，從長遠來看，仔細研究該理論并真正在該理論上投入時間是必不可少且無價的。 您將更好地了解機器學習的新進展，并且能夠鏈接回您的背景知識。 這將幫助您保持動力。

Additionally, the theory doesn’t need to be boring. As you read in the introduction, there are so many materials that will make it easier for you to get into it.

此外，該理論不必很無聊。 正如您在簡介中所讀到的那樣，有太多材料可以使您更輕松地入門。

Books are one of the best ways to absorb the theoretical knowledge. They force you to stop and think once in a while. Of course, reading books is a very static thing to do and it might not agree with your learning style. Nonetheless, try out the following books and see if it might be something for you:

書籍是吸收理論知識的最佳方法之一。 他們迫使您停下來思考一下。 當然，讀書是一件非常靜態(tài)的事情，可能與您的學習風格不一致。 盡管如此，請嘗試以下書籍，看看是否適合您：

• Machine Learning textbook, by Tom Mitchell might be old but it’s gold. This book goes over the most important topics in machine learning in a well-explained and step-by-step way.

  湯姆·米切爾(Tom Mitchell)撰寫的《 機器學習》教科書雖然年代久遠，但卻是黃金。 本書以詳盡的解釋和分步介紹了機器學習中最重要的主題。

• Machine Learning: The Art and Science of Algorithms that Make Sense of Data (you can see the slides of the book here): this book is great for beginners. There are many real-life applications discussed, which you might find lacking in Tom Mitchell’s book.

  機器學習：具有數(shù)據(jù)意義的算法的藝術(shù)和科學 (您可以在此處查看本書的幻燈片)：本書非常適合初學者。 討論了許多現(xiàn)實生活中的應用程序，您可能會在Tom Mitchell的書中發(fā)現(xiàn)這些應用程序缺乏。

• Machine Learning Yearning: this book by Andrew Ng is not yet complete, but it’s bound to be an excellent reference for those who are learning ML.

  機器學習的渴望 ：Andrew Ng的這本書尚未完成，但對于學習ML的人來說無疑是一個很好的參考。

• Algorithms and Data Structures by Jurg Nievergelt and Klaus Hinrichs

  Jurg Nievergelt和Klaus Hinrichs的算法和數(shù)據(jù)結(jié)構(gòu)

• Also check out the Data Mining for the Masses by Matthew North. You’ll find that this book guides you through some of the most difficult topics.

  還可以查看Matthew North 的《大眾數(shù)據(jù)挖掘》 。 您會發(fā)現(xiàn)這本書指導您完成一些最困難的主題。

• Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan.

  Alex Smola和SVN Vishwanathan撰寫的機器學習入門 。

Videos / MOOCs are awesome for those who learn by watching and listening. There are a lot of MOOCs and videos out there, but it can also be hard to find your way through all those materials. Below is a list of the most notable ones:

對于那些通過觀看和收聽來學習的人來說， 視頻/ MOOC非常棒。 那里有很多MOOC和視頻，但是在所有這些材料中也很難找到自己的方式。 以下是最著名的列表：

• This well-known Machine Learning MOOC, taught by Andrew Ng, introduces you to Machine Learning and the theory. Don’t worry — it’s well-explained and takes things step-by-step, so it’s excellent for beginners.

  這是由Andrew Ng教授的著名的機器學習MOOC ，向您介紹了機器學習和理論。 不用擔心-它經(jīng)過充分解釋，并且可以逐步進行，因此對于初學者來說非常好。

• The playlist of the MIT Open Courseware 6034 course: already a bit more advanced. You’ll definitely need some previous work on ML theory before you start this series, but you won’t regret it.

  麻省理工學院開放課件6034課程的播放列表 ：已經(jīng)有點高級了。 在開始本系列之前，您肯定需要先于機器學習理論進行一些工作，但是您不會后悔。

At this point, it’s important for you to go over the separate techniques and grasp the whole picture. This starts with understanding key concepts: the distinction between supervised and unsupervised learning, classification and regression, and so on. Manual (written) exercises can come in handy. They can help you understand how algorithms work and how you should go about them. You’ll most often find these written exercises in courses from universities. Check out this ML course by Portland State University.

在這一點上，對您來說，重要的是要復習單獨的技術(shù)并掌握整個情況。 首先要理解關(guān)鍵概念：有監(jiān)督和無監(jiān)督學習之間的區(qū)別，分類和回歸等等。 手動(書面)練習可以派上用場。 它們可以幫助您了解算法如何工作以及應該如何使用它們。 您通常會在大學課程中找到這些書面練習。 看看波特蘭州立大學的ML課程 。

3.動手 (3. Get hands-on)

Knowing the theory and understanding the algorithms by reading and watching is all good. But you also need to surpass this stage and get started with some exercises. You’ll learn to implement these algorithms and apply the theory that you’ve learned.

通過閱讀和觀看知識了解理論并理解算法都是很好的。 但是您還需要超越此階段并開始一些練習。 您將學習實現(xiàn)這些算法并應用所學的理論。

First, you have tutorials which introduce you to the basics of machine learning in Python and R. The best way is, of course, to go for interactive tutorials:

首先，您有一些教程，向您介紹Python和R中的機器學習基礎。當然，最好的方法是進行交互式教程：

• In Python Machine Learning: Scikit-Learn Tutorial, you will learn more about well-known algorithms KMeans and Support Vector Machines (SVM) to construct models with Scikit-Learn.

  在“ Python機器學習：Scikit-Learn教程”中 ，您將學到更多有關(guān)使用Scikit-Learn構(gòu)造模型的著名算法KMeans和支持向量機(SVM)的信息。

• Machine Learning in R for beginners introduces you to ML in R with the class and caret packages.

  面向初學者的R語言機器學習通過類和插入符號包向您??介紹R語言中的ML。

• Keras Tutorial: Deep Learning in Python covers how to build Multi-Layer Perceptrons (MLPs) for classification and regression tasks, step-by-step.

  Keras教程：Python深度學習涵蓋了如何逐步構(gòu)建用于分類和回歸任務的多層感知器(MLP)。

Also check out the following tutorials, which are static and will require you to work in an IDE:

另請查看以下教程，這些教程是靜態(tài)的，需要您在IDE中工作：

• Machine Learning in Python, Step By Step: step-by-step tutorial with Scikit-Learn.

  使用Python進行機器學習的分步指南：Scikit-Learn的分步教程。

• Develop Your First Neural Network in Python With Keras Step-By-Step: learn how to develop your first neural network with Keras thanks to this tutorial.

  使用Keras用Python開發(fā)您的第一個神經(jīng)網(wǎng)絡循序漸進 ：借助本教程，學習如何使用Keras開發(fā)第一個神經(jīng)網(wǎng)絡。

• There are many more that you can consider, but the tutorials of Machine Learning Mastery are very good.

  您可以考慮更多的內(nèi)容，但是機器學習精通的教程非常好。

Besides the tutorials, there are also courses. Taking courses will help you apply the concepts that you’ve learned in a focused way. Experienced instructors will help you. Here are some interactive courses for Python and ML:

除了教程，還有課程。 參加課程將幫助您集中精力應用所學的概念。 經(jīng)驗豐富的教練將為您提供幫助。 以下是一些針對Python和ML的交互式課程：

• Supervised Learning with scikit-learn: you’ll learn how to build predictive models, tune their parameters, and predict how well they will perform on unseen data. All while using real world datasets. You’ll do so with Scikit-Learn.

  使用scikit-learn進行監(jiān)督學習 ：您將學習如何構(gòu)建預測模型，調(diào)整其參數(shù)以及預測它們在看不見的數(shù)據(jù)上的表現(xiàn)如何。 始終使用真實世界的數(shù)據(jù)集。 您將使用Scikit-Learn進行操作。

• Unsupervised Learning in Python: shows you how to cluster, transform, visualize, and extract insights from unlabeled datasets. At the end of the course, you’ll build a recommender system.

  Python中的無監(jiān)督學習 ：向您展示如何從未標記的數(shù)據(jù)集中進行聚類，轉(zhuǎn)換，可視化和提取見??解。 在課程結(jié)束時，您將構(gòu)建一個推薦系統(tǒng)。

• Deep Learning in Python: you’ll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

  使用Python進行深度學習 ：您將獲得有關(guān)如何在Keras 2.0中使用深度學習的動手實踐知識，Keras 2.0是用于Python深度學習的前沿庫的最新版本。

• Applied Machine Learning in Python: introduces the learner to applied ML and focuses more on the techniques and methods than on the statistics behind these methods.

  Python中的應用機器學習 ：向?qū)W習者介紹應用機器學習，并更多地側(cè)重于技術(shù)和方法，而不是這些方法背后的統(tǒng)計數(shù)據(jù)。

For those who are learning ML with R, there are also these interactive courses:

對于那些正在學習R的ML的人，還提供以下交互式課程：

• Introduction to Machine Learning gives you a broad overview of the discipline’s most common techniques and applications. You’ll gain more insight into the assessment and training of different ML models. The rest of the course focuses on an introduction to three of the most basic ML tasks: classification, regression, and clustering.

  機器學習入門為您提供了該學科最常見的技術(shù)和應用的廣泛概述。 您將獲得有關(guān)不同ML模型的評估和培訓的更多見解。 本課程的其余部分重點介紹三個最基本的ML任務：分類，回歸和聚類。

• R: Unsupervised Learning provides a basic introduction to clustering and dimensionality reduction in R from a ML perspective. This allows you to get from data to insights as quickly as possible.

  R：無監(jiān)督學習從ML的角度為R中的聚類和降維提供了基本的介紹。 這使您可以盡快從數(shù)據(jù)獲取見解。

• Practical Machine Learning covers the basic components of building and applying prediction functions with an emphasis on practical applications.

  實用機器學習涵蓋了構(gòu)建和應用預測功能的基本組件，重點是實際應用。

Lastly, there are also books that go over ML topics in a very applied way. If you’re looking to learn with the help of text and an IDE, check out these books:

最后，還有一些書籍以非常實用的方式討論了ML主題。 如果您想借助文本和IDE來學習，請查看以下書籍：

• The Python Machine Learning Book by Sebastian Raschka

  Sebastian Raschka撰寫的Python機器學習書

• The Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python by Sebastian Raschka

  人工神經(jīng)網(wǎng)絡和深度學習簡介： Sebastian Raschka撰寫的Python應用指南

• Machine Learning with R by Brett Lantz

  使用 Brett Lantz的R進行機器學習

4.實踐 (4. Practice)

Practice is even more important than getting hands-on and revising the material with Python. This step was probably the hardest one for me. Check out how other people have implemented ML algorithms when you have done some exercises. Then, get started on your own projects that illustrate your understanding of ML algorithms and theories.

實踐甚至比動手動手并用Python修改材料更重要。 這一步對我來說可能是最難的一步。 完成一些練習后，請查看其他人如何實現(xiàn)ML算法。 然后，從您自己的項目開始，這些項目說明您對ML算法和理論的理解。

One of the most straightforward ways is to see the exercises a tiny bit bigger. You want to do a bigger exercise which requires you to do more data cleaning and feature engineering.

最直接的方法之一是查看練習稍大一點。 您想做一個更大的練習，這需要您做更多的數(shù)據(jù)清理和功能設計。

• Start with Kaggle. If you need additional help to conquer the so-called “data fear,” check out the Kaggle Python Tutorial on Machine Learning and Kaggle R Tutorial on Machine Learning. These will bring you up to speed in no time.

  從Kaggle開始。 如果您需要其他幫助來克服所謂的“數(shù)據(jù)恐懼”，請查看有關(guān)機器學習的Kaggle Python教程和有關(guān)機器學習的 Kaggle R教程 。 這些將使您立即加快速度。

• Afterwards, you can also start doing challenges by yourself. Check out these sites, where you can find lots of ML datasets: UCI Machine Learning Repository, Public datasets for machine learning, and data.world.

  之后，您也可以自己開始挑戰(zhàn)。 查看這些站點，您可以在其中找到許多ML數(shù)據(jù)集： UCI機器學習存儲庫 ， 用于機器學習的公共數(shù)據(jù)集和data.world 。

Tip: don’t forget that there are handy resources to help you out when you’re practicing — Check out these data science cheat sheets.

提示 ：在練習時，請不要忘記有方便的資源來幫助您-查看這些數(shù)據(jù)科學備忘單 。

5.項目 (5. Projects)

Doing small exercises is good. But in the end, you’ll want to make a project in which you can demonstrate your understanding of the ML algorithms with which you’ve been working.

做些小運動是好的。 但是最后，您將需要創(chuàng)建一個項目，在其中可以證明您對正在使用的ML算法的理解。

The best exercise is to implement your own ML algorithm. You can read more about why you should do this exercise and what you can learn from it in the following pages:

最好的練習是實現(xiàn)自己的ML算法。 您可以在以下頁面中閱讀有關(guān)為什么要進行此練習的更多信息，以及可以從中學到的知識：

• Why is there a need to manually implement machine learning algorithms when there are many advanced APIs like tensorflow available?

  當有許多高級API(如tensorflow)可用時，為什么需要手動實現(xiàn)機器學習算法？

• Why Implement Machine Learning Algorithms From Scratch?

  為什么要從頭開始實施機器學習算法？

• What I Learned Implementing a Classifier from Scratch in Python

  我從Python的Scratch實現(xiàn)分類器中學到的知識

Next, you can check out the following posts and repositories. They’ll give you some inspiration from others and will show how they have implemented ML algorithms.

接下來，您可以查看以下帖子和存儲庫。 他們將從其他人那里給您一些啟發(fā)，并說明他們?nèi)绾螌崿F(xiàn)ML算法。

• How to Implement a Machine Learning Algorithm

  如何實現(xiàn)機器學習算法

• ML From Scratch

  ML從頭開始

• Machine Learning Algorithms From Scratch

  從頭開始的機器學習算法

6.不要停止 (6. Don’t stop)

Learning ML is something that should never stop. As many will confirm, there are always new things to learn — even when you’ve been working in this area for a decade.

學習機器學習是永不停息的。 正如許多人會確認的那樣，即使您已經(jīng)在這一領(lǐng)域工作了十年，也總是有新的東西需要學習。

There are, for example, ML trends such as deep learning which are very popular right now. You might also focus on other topics that aren’t central at this point but which might be in the future. Check out this interesting question and the answers if you want to know more.

例如， 機器學習趨勢(例如深度學習)目前非常流行。 您可能還會專注于目前尚不重要但將來可能會涉及的其他主題。 如果您想了解更多信息，請查看此有趣的問題和答案 。

Papers may not be the first thing that spring to mind when you’re worried about mastering the basics. But they are your way to get up to date with the latest research. Papers are not for those who are just starting out. They are definitely a good fit for those who are more advanced.

當您擔心掌握基礎知識時，想到的第一件事可能不是論文 。 但是，它們是您了解最新研究的方法。 論文不適合那些剛剛起步的人。 它們絕對適合更高級的人。

• Top 20 Recent Research Papers on Machine Learning and Deep Learning

  最近關(guān)于機器學習和深度學習的20篇研究論文

• Journal of Machine Learning Research

  機器學習研究雜志

• Awesome Deep Learning Papers

  很棒的深度學習論文

• What are some of the best research papers/books for Machine learning?

  關(guān)于機器學習的最佳研究論文/書有哪些？

Other technologies are also something to consider. But don’t worry about them when you’re just starting out. You can, for example, focus on adding Python or R (depending on which one you already know) to your skill set. You can look through this post to find interesting resources.

其他技術(shù)也是要考慮的東西。 但是，當您剛?cè)腴T時，不必擔心它們。 例如，您可以集中精力將Python或R(取決于您已經(jīng)知道的哪個)添加到您的技能組合中。 您可以瀏覽這篇文章以找到有趣的資源。

If you also want to move towards big data, you could consider looking into Spark. Here are some interesting resources:

如果您還想轉(zhuǎn)向大數(shù)據(jù)，則可以考慮研究Spark。 以下是一些有趣的資源：

• Introduction to Spark in R with sparklyr

  使用Sparklyr的R中的Spark簡介

• Data Science And Engineering With Spark

  Spark的數(shù)據(jù)科學與工程

• Introduction to Apache Spark

  Apache Spark簡介

• Distributed Machine Learning with Apache Spark

  使用Apache Spark進行分布式機器學習

• Big Data Analysis with Apache Spark

  使用Apache Spark進行大數(shù)據(jù)分析

• Apache Spark in Python: Beginner’s Guide

  Python中的Apache Spark：新手指南

• PySpark RDD Cheat Sheet

  PySpark RDD速查表

• PySpark SQL Cheat Sheet.

  PySpark SQL備忘單 。

Other programming languages, such as Java, JavaScript, C, and C++ are gaining importance in ML. In the long run, you can consider also adding one of these languages to your to-do list. You can use these blog posts to guide your choice:

其他編程語言(例如Java，JavaScript，C和C ++)在ML中正變得越來越重要。 從長遠來看，您還可以考慮將這些語言之一添加到您的工作清單中。 您可以使用這些博客文章來指導您的選擇：

• Most Popular Programming Languages for Machine Learning and Data Science

  機器學習和數(shù)據(jù)科學最流行的編程語言

• The Most Popular Language For Machine Learning And Data Science Is…

  機器學習和數(shù)據(jù)科學最流行的語言是……

7.利用那里的所有材料 (7. Make use of all the material that is out there)

Machine learning is a difficult topic which can make you lose your motivation at some point. Or maybe you feel you need a change. In such cases, remember that there’s a lot of material on which you can fall back. Check out the following resources:

機器學習是一個困難的話題，它會使您在某些時候失去動力。 也許您覺得自己需要改變。 在這種情況下，請記住，有很多材料可以依靠。 查看以下資源：

Podcasts. Great resource for continuing your journey into ML and staying up-to-date with the latest developments in the field:

播客 。 繼續(xù)學習ML并保持該領(lǐng)域最新動態(tài)的寶貴資源：

• DataFramed

  數(shù)據(jù)框架

• Talking Machines

  會說話的機器

• Data Skeptic

  數(shù)據(jù)懷疑者

• Linear Digressions

  線性離題

• This Week in Machine Learning & AI

  本周機器學習和AI

• Learning Machines 101

  學習機101

There are, of course, many more podcasts, but this list is just to get you started!

當然，還有更多的播客，但是此列表只是為了幫助您入門！

Documentation and package source code are two ways to get deeper into the implementation of the ML algorithms. Check out some of these repositories:

文檔和程序包源代碼是深入了解ML算法實現(xiàn)的兩種方法。 查看以下一些存儲庫：

• Scikit- Learn: Well-known Python ML package

  Scikit - Learn ：著名的Python ML軟件包

• Keras: Deep learning package for Python

  Keras ：Python深度學習軟件包

• caret: very popular R package for Classification and Regression Training

  插入符 ：非常流行的用于分類和回歸訓練的R包

Visualizations are one of the newest and trendiest ways to get into the theory of ML. They’re fantastic for beginners, but also very interesting for more advanced learners. The following visualizations will intrigue you and will help you gain more understanding into the workings of ML:

可視化是進入ML理論的最新方式。 對于初學者來說，它們很棒，但是對于更高級的學習者來說，它們也非常有趣。 以下可視化效果會吸引您，并會幫助您進一步了解ML的工作原理：

• A visual introduction to machine learning

  機器學習的視覺介紹

• Distill makes ML Research clear, dynamic and vivid.

  Distill使ML Research清晰，動態(tài)和生動。

• Tensorflow — Neural Network Playground if you’re looking to play around with neural network architectures.

  Tensorflow —神經(jīng)網(wǎng)絡游樂場，如果您正在嘗試使用神經(jīng)網(wǎng)絡體系結(jié)構(gòu)。

• More here: What are the best visualizations of machine learning algorithms?

  更多內(nèi)容： 機器學習算法的最佳可視化是什么？

您可以立即開始 (You Can Get Started Now)

Now it’s up to you. Learning ML is something that’s a continuous process, so the sooner you get started, the better. You have all of the tools in your hands now to get started. Good luck and make sure to let us know how you’re progressing.

現(xiàn)在由您決定。 學習機器學習是一個連續(xù)的過程，因此，越早開始越好。 現(xiàn)在，您已經(jīng)掌握了所有工具以開始使用。 祝您好運，并確保讓我們知道您的進度。

This post is based on an answer I gave to the Quora question How Does A Total Beginner Start To Learn Machine Learning.

這篇文章基于我對Quora問題的回答，即一個完全的初學者如何開始學習機器學習 。

翻譯自: https://www.freecodecamp.org/news/how-machines-learn-a-practical-guide-203aae23cafb/

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