{"id":3813,"date":"2020-12-15T12:41:00","date_gmt":"2020-12-15T11:41:00","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/uncategorized\/best-machine-learning-languages\/"},"modified":"2023-07-25T11:05:37","modified_gmt":"2023-07-25T09:05:37","slug":"best-machine-learning-languages","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/best-machine-learning-languages\/","title":{"rendered":"What\u2019s the Best Language for Machine Learning?"},"content":{"rendered":"<p><strong>In this article, I&#8217;ll go through some of the most common programming languages used in the field of machine learning. Which one is right for you?<\/strong><\/p>\n<p><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/types-of-ai\/\" target=\"_blank\" rel=\"noopener\">A part of artificial intelligence (AI)<\/a>, machine learning is a complex but exciting field. Many data experts dedicate their careers to mastering it. If you\u2019re new to data analytics or data science and have an interest in machine learning, there are some particular skills you\u2019ll need to develop. Besides theoretical knowledge, this includes some basic understanding of programming.<\/p>\n<p>But with hundreds available, which is the best language for machine learning? In this post, we\u2019ll explore what machine learning involves, before looking at some programming languages you might want to consider adding to your repertoire. We\u2019ll cover:<\/p>\n<ol>\n<li><strong><a href=\"#what-is-machine-learning\">What is machine learning?<\/a><\/strong><\/li>\n<li><strong><a href=\"#what-skills-are-important-for-machine-learning\">What skills are important for machine learning?<\/a><\/strong><\/li>\n<li><strong><a href=\"#five-of-the-top-machine-learning-programming-languages\">Five of the top machine learning programming languages<\/a><\/strong>\n<ul>\n<li><a href=\"#python\">Python<\/a><\/li>\n<li><a href=\"#r\">R<\/a><\/li>\n<li><a href=\"#c++\">C++<\/a><\/li>\n<li><a href=\"#java\">Java<\/a><\/li>\n<li><a href=\"#javascript\">JavaScript<\/a><\/li>\n<\/ul>\n<\/li>\n<li><strong><a href=\"#in-summary\">Wrap-up<\/a><\/strong><\/li>\n<\/ol>\n<p>First up: what is machine learning?<\/p>\n<h2 id=\"what-is-machine-learning\">1. What is machine learning?<\/h2>\n<p>To put it simply, Machine learning (ML) is the study of computer algorithms that learn without being explicitly programmed by humans. It&#8217;s a subset of <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/types-of-ai\/\" target=\"_blank\" rel=\"noopener\">artificial intelligence (AI)<\/a>.<\/p>\n<p>Although ML algorithms start with basic instructions from their human designers, they learn and make predictions on their own. They achieve this by ingesting training data, which helps them to identify patterns and trends. This information can be used in a broad variety of ways, as we\u2019ll see in this article.<\/p>\n<p><strong>Learn more:<\/strong> <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-machine-learning\/\" target=\"_blank\" rel=\"noopener\">A Beginner&#8217;s Guide to what is Machine Learning<\/a><\/p>\n<p><strong>Watch:<\/strong> <a href=\"https:\/\/www.youtube.com\/watch?v=i4vKOCMgdgc\" target=\"_blank\" rel=\"noopener\">What Exactly is Machine Learning? (Video)<\/a><\/p>\n<h3 id=\"when-do-we-use-machine-learning\">When do we use machine learning?<\/h3>\n<p>We use machine learning in cases when it\u2019s not practical for humans to create specific algorithms. This is usually because there\u2019s so much data to work through that it would take a person countless lifetimes to do the job manually! With <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/is-big-data-dangerous\/\">big data flooding our lives<\/a>, machine learning is an increasing necessity. But how does it work in practice?<\/p>\n<p>For starters, let\u2019s look at the field of <strong>natural language processing (NLP)<\/strong>. This is where <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-are-nlp-algorithms\/\" target=\"_blank\" rel=\"noopener\">NLP algorithms<\/a> learn to understand the contextual nuances of human language. NLP has applications from language translation to internet search. Email providers even use it for spam filtering.<\/p>\n<p>Machine learning is also used for <strong>computer vision<\/strong>. This is where algorithms ingest digital images or video to make sense of those data. This can help in areas like medicine, to diagnose patients based on their scans. By analyzing visual data, we can also program the navigation systems in autonomous vehicles, like self-driving cars or military drones.<\/p>\n<p>From credit card fraud to solving complex mathematical problems, machine learning has countless uses. In short, it is a huge part of the world we live in, and it is growing. If you\u2019re considering a career in machine learning, now\u2019s a great time to dip your toe in, as <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-engineer-salary\/\" target=\"_blank\" rel=\"noopener\">machine learning salaries<\/a> will testify!<\/p>\n<h2 id=\"what-skills-are-important-for-machine-learning\">2. What skills are important for machine learning?<\/h2>\n<p>If you\u2019re entering the world of machine learning, you\u2019ll need to cultivate some core data analytics skills.<\/p>\n<p>This includes knowledge of at least one programming language. Machine learning involves manipulating data in very specific ways. You\u2019ll need to prototype algorithms and understand the internal mechanisms behind ML concepts.<\/p>\n<p>Programming is integral to this. <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-engineer\/\" target=\"_blank\" rel=\"noopener\">Machine learning engineers<\/a> probably spend more time writing code than developing statistical models. And to communicate with computers, we need at least basic coding skills.<\/p>\n<p>However, <strong>the language you learn is secondary to mastering basic machine learning concepts<\/strong>. Without a fundamental knowledge of statistics, deep learning, systems process and design (and so on) you\u2019ll never know how to choose the right models or solve ML problems. So put machine learning theory at the top of your to-do list. However, presuming you\u2019re well underway with this, where next for your programming skills?<\/p>\n<p>Here&#8217;s a collection of <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-skills\/\" target=\"_blank\" rel=\"noopener\">12 of the most important machine learning skills to learn<\/a>.<\/p>\n<p>If you\u2019re new to data analytics and machine learning, consider <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/is-python-a-good-language-to-learn\/\">learning a language like Python<\/a>. Python is syntactically straightforward and easy to learn. If you\u2019re already an experienced programmer with years of experience in say, C++, it might be better to stick with what you know. The truth is that <strong>there is no one \u2018correct\u2019 language to learn for machine learning<\/strong>. But there are some languages that are more in vogue than others. Let\u2019s look at some of these next.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-9692\" src=\"http:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2020\/12\/person-exploring-machine-learning-library.jpeg\" alt=\"Person exploring machine learning languages\" width=\"1200\" height=\"600\" title=\"\" srcset=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2020\/12\/person-exploring-machine-learning-library.jpeg 1200w, https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2020\/12\/person-exploring-machine-learning-library-300x150.jpeg 300w, https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2020\/12\/person-exploring-machine-learning-library-1024x512.jpeg 1024w, https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-content\/uploads\/2020\/12\/person-exploring-machine-learning-library-768x384.jpeg 768w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/p>\n<h2 id=\"five-of-the-top-machine-learning-programming-languages\">3. Five of the top machine learning programming languages<\/h2>\n<p>In 2023, GitHub <a href=\"https:\/\/github.blog\/2023-11-08-the-state-of-open-source-and-ai\/#the-most-popular-programming-languages\" target=\"_blank\" rel=\"noopener\">surveyed the top ten machine learning programming languages<\/a> on their platform. From R to Java and C++, we\u2019ve selected five of our favorites to explore more closely.<\/p>\n<h3 id=\"python\">1. Python for machine learning<\/h3>\n<h4 id=\"what-is-python\">What is Python?<\/h4>\n<p>A high-level, general-purpose programming language, Python is an easy one to learn. Its popularity has boomed in recent years, taking it ahead of C++ in fields like data analytics and machine learning. Python\u2019s straightforward syntax and speed to competence make it excellent to learn and great for fast prototyping.<\/p>\n<p>As a high-level language (like JavaScript) Python executes more slowly than some languages. It makes up for this with a huge array of libraries for everything from gaming and special effects to data analytics, AI, and ML. Even if you already use a more complex language, Python is still worth learning. Its agility and ubiquitous nature mean it\u2019s a string well worth adding to your bow. If you\u2019re keen to add this language to your repertoire, check out <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/how-to-learn-python\/\">our top tips for learning Python<\/a>.<\/p>\n<p>CareerFoundry\u2019s\u00a0<a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/courses\/machine-learning-with-python\/\"><strong>Machine Learning with Python course<\/strong><\/a>\u00a0is designed to ease you into this exciting area of data analytics. Possible as a standalone course as well as a specialization within our full Data Analytics Program, you\u2019ll learn and apply the machine learning skills and develop the experience needed to stand out from the crowd.<\/p>\n<h4 id=\"how-is-python-used-in-machine-learning\">How is Python used in machine learning?<\/h4>\n<p>In machine learning, Python has similar applications to Java. However, it is often used in more scientific, less-enterprise-focused areas, e.g. <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/sentiment-analysis\/\" target=\"_blank\" rel=\"noopener\">sentiment analysis<\/a> and natural language processing. Python\u2019s recent surge in popularity is closely linked to the fact that it has evolved alongside the field of data science. They are now almost symbiotic as a result.<\/p>\n<p>Python\u2019s standout feature is the Python Package Index. This contains thousands of libraries of code, many of which have been <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/python-machine-learning-libraries\/\" target=\"_blank\" rel=\"noopener\">specifically created for machine learning<\/a>. For example, TensorFlow allows beginners and experts alike to train ML algorithms with minimal effort.<\/p>\n<p><a href=\"https:\/\/keras.io\/\" rel=\"noopener\">Keras<\/a> is a popular neural network library, while <a href=\"https:\/\/nltk.org\/\" rel=\"noopener\">NLTK<\/a> (short for Natural Language Toolkit) is great for working with language data. While Python is not the fastest language to execute, for those interested in scientific computing and ML, it is the gold standard.<\/p>\n<p><strong>Learn more:<\/strong> <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/pytorch-vs-tensorflow\/\">PyTorch vs TensorFlow: What Are They, and Which Should You Use?<\/a><\/p>\n<h3 id=\"r\">2. R for machine learning<\/h3>\n<h4 id=\"what-is-r\">What is R?<\/h4>\n<p>R is a <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/web-development\/functional-programming-vs-oop\/\" target=\"_blank\" rel=\"noopener\">functional programming language<\/a>, often used for <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/different-types-of-data-analysis\/\">data analysis<\/a> and visualizations. It\u2019s popular with scientists, statisticians, and others in the academic community. Derived from an older language, S, it was first developed in the early 90s at Auckland University in New Zealand. It has since grown and now includes support for object-oriented programming (a design principle that is important for, amongst other things, machine learning).<\/p>\n<p>The fact that R is so popular with statisticians explains, in part, why it\u2019s also so popular in the ML community. One of R\u2019s main strengths is its large number of user-created extension packages, which allow users to apply specialized statistical techniques. There are currently over 15,000 packages available on the <a href=\"https:\/\/cran.r-project.org\/\" rel=\"noopener\">Comprehensive R Archive Network (CRAN)<\/a>.<\/p>\n<h4 id=\"how-is-r-used-in-machine-learning\">How is R used in machine learning?<\/h4>\n<p>In machine learning, R is often used as a supplementary tool to support other languages. However, it\u2019s also popular in its own right for tasks like sentiment analysis. R is commonly used in scientific fields like bioengineering (designing and testing medical equipment), bioinformatics (the study of large amounts of biological data), and ecology. But it is well-suited to any machine learning task that is heavy on statistics.<\/p>\n<p>There are many R packages designed to streamline data-heavy machine learning tasks. For instance, the <a href=\"http:\/\/topepo.github.io\/caret\/index.html\" rel=\"noopener\">Classification and Regression Training (caret)<\/a> package makes creating predictive models far easier. <a href=\"https:\/\/www.rdocumentation.org\/packages\/randomForest\/versions\/4.6-14\/topics\/randomForest\" rel=\"noopener\">Randomforest<\/a> can create random forest algorithms using <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-a-decision-tree\/\">decision trees<\/a>. Meanwhile, packages like <a href=\"https:\/\/ggplot2.tidyverse.org\/\" rel=\"noopener\">ggplot2<\/a> and <a href=\"https:\/\/plotly.com\/\" rel=\"noopener\">plotly<\/a> are excellent for data viz.<\/p>\n<h3 id=\"c++\">3. C++ for machine learning<\/h3>\n<h4 id=\"what-is-c\">What is C++?<\/h4>\n<p>C++ is an object-oriented, general-purpose programming language. Launched in the 1980s as a systems language (for building system architectures) it is complex to learn but has proven popular for performance-critical jobs. It\u2019s now used to create desktop applications, video games and even to program <a href=\"https:\/\/isocpp.org\/blog\/2015\/01\/cppcon-2014-c-on-mars-incorporating-c-into-mars-rover-flight-software-mark\" rel=\"noopener\">Martian space rovers<\/a>. Pretty cool!<\/p>\n<p>C++ has many applications largely because it is a low-level language. This means it communicates with machines close to their native code. (The alternative is a high-level, abstract language, like Python, which is easier to use but slower to execute). Being low level, C++ has a steep learning curve. But it is also excellent for memory manipulation. Speed here is key.<\/p>\n<h4 id=\"how-is-c-used-in-machine-learning\">How is C++ used in machine learning?<\/h4>\n<p>In terms of machine learning, C++ users can manipulate algorithms and manage memory resources at a granular level. That\u2019s why it lends itself so well to areas like AI, where speed is critical for analyzing large datasets. The trade-off is that C++ is not great for quick prototyping. Even so, it remains a top favorite among data analysts and machine learning engineers.<\/p>\n<p>Because C++ offers close control over performance, it\u2019s popular in areas like robotics and gaming, which need high responsiveness. These are also areas where machine learning is growing fast. What\u2019s more, C++ has several sophisticated artificial intelligence and machine learning libraries. These include the deep learning framework, <a href=\"https:\/\/caffe.berkeleyvision.org\/\" rel=\"noopener\">Caffe<\/a>, the neural network library, <a href=\"http:\/\/dynet.io\/\" rel=\"noopener\">DyNet<\/a>, and <a href=\"https:\/\/www.shogun-toolbox.org\/\" rel=\"noopener\">Shogun<\/a>, an open-source ML library with lots of different models to play with.<\/p>\n<h3 id=\"java\">4. Java for machine learning<\/h3>\n<h4 id=\"what-is-java\">What is Java?<\/h4>\n<p>Like C++, Java is an object-oriented language. Its syntax is similarly complex to C++, although it doesn\u2019t work at such a low level. Java is also a general-purpose programming language. It\u2019s used to create applications that run on any platform, via the Java Virtual Machine (a kind of system emulator). It\u2019s commonly used to create applets for web pages, large-scale enterprise systems, and apps on the Android mobile platform.<\/p>\n<p>Java has a long history in the professional sphere. Its users traditionally worked in financial institutions and the enterprise industry. It\u2019s now often used in areas like network\/ cybersecurity and fraud detection. Many who use Java for machine learning do so because they\u2019re used to applying it on enterprise development projects.<\/p>\n<h4 id=\"how-is-java-used-in-machine-learning\">How is Java used in machine learning?<\/h4>\n<p>Java is highly scalable. This makes it great for creating complex, large-scale ML algorithms. Many big data frameworks like Hadoop, Hive, and Spark (used for ML) are also Java-based. The Java Virtual Machine allows users to create <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-tools\/\">machine learning tools<\/a> fast and roll them out at speed. It\u2019s also quick to execute. For all these reasons, tech giants like Twitter, LinkedIn, and Facebook all use Java to manage big data.<\/p>\n<p>Java also has several machine learning libraries and tools. <a href=\"https:\/\/www.cs.waikato.ac.nz\/ml\/weka\/\" rel=\"noopener\">Weka<\/a>, for instance, is a Java workbench used for data mining, analysis, predictive modeling, and visualization. The <a href=\"https:\/\/moa.cms.waikato.ac.nz\/\" rel=\"noopener\">Massive Online Analysis (MOA)<\/a> framework is used for data stream mining and contains ML algorithms for things like classification, regression, clustering, and more. You can <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/regression-vs-classification\/\">learn more about classification and regression for predictive analytics in this post.<\/a><\/p>\n<h3 id=\"javascript\">5. JavaScript for machine learning<\/h3>\n<h4 id=\"what-is-javascript\">What is JavaScript?<\/h4>\n<p>Unlike the languages we\u2019ve looked at so far, <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/web-development\/introduction-to-javascript\/\">JavaScript is a high-level language<\/a>. This means that its syntax is much simpler to grasp, making it easier to learn. JavaScript was originally designed as a scripting language. It has since evolved into a general-purpose programming language that you\u2019ll commonly find running in your browser, in the form of things like pop-up messages or live clocks.<\/p>\n<p>While JavaScript is great for frontend work, it also runs on the backend (or server-side). Here, it is often used as an API for apps built using languages that may lack JavaScript\u2019s high-level functionality. And in case you were wondering, no: JavaScript is no relation to Java! They have different design principles and are maintained by different organizations. But it was worth asking!<\/p>\n<h4 id=\"how-is-javascript-used-in-machine-learning\">How is JavaScript used in machine learning?<\/h4>\n<p>While JavaScript lacks the speed of lower-level languages, its web applications are useful for ML. For instance, developers often funnel output from machine learning algorithms into Java-based web dashboards. However, it is less well-suited to labor-intensive tasks. While ML often needs to crunch complex numbers, JavaScript contains fairly basic mathematical functionality.<\/p>\n<p>With that said, the number of JavaScript libraries for machine learning is growing. For instance, <a href=\"https:\/\/mathjs.org\/\" rel=\"noopener\">math.js<\/a> provides the language with far more mathematical flexibility and computing power. It also now supports packages built for other languages like <a href=\"https:\/\/www.tensorflow.org\/js\" rel=\"noopener\">TensorFlow.js<\/a> (originally made for Python). This allows JavaScript developers to run\/ retrain existing ML models, and to create new ones. It also boasts neural network libraries, like <a href=\"https:\/\/caza.la\/synaptic\/#\/\" rel=\"noopener\">Synaptic<\/a> (which emulates the functionality of the brain) and image processing tools like <a href=\"https:\/\/docs.opencv.org\/3.4\/df\/d0a\/tutorial_js_intro.html\" rel=\"noopener\">OpenCV.js<\/a>. Watch this space, because JavaScript isn\u2019t going away!<\/p>\n<h2 id=\"in-summary\">4. In summary<\/h2>\n<p>In this article, we\u2019ve explored the importance of programming in ML and gone through 5 of the best languages for machine learning. We\u2019ve learned that, while there\u2019s no single &#8220;best&#8221; language to learn, some are more suited to machine learning tasks than others. We now know that:<\/p>\n<ul>\n<li>Machine learning is the study of computer algorithms that learn without human input.<\/li>\n<li>ML has countless applications, from natural language processing to computer vision, neural networks, predictive analytics, and more.<\/li>\n<li>Lower-level languages (like R, C++, or Java) offer greater speed but are harder to learn.<\/li>\n<li>Higher-level languages (like JavaScript and Python) are easier to use but slower to execute.<\/li>\n<li>Python is a key language for machine learning and data analytics. For speed-to-competence and breadth of application, it\u2019s probably the best one for beginners.<\/li>\n<li>Nevertheless, the right language hinges on the problem you\u2019re solving, your expertise, and your programming experience. Don\u2019t limit yourself!<\/li>\n<\/ul>\n<p>Machine learning is just one of many exciting career paths you could pursue once you have a foundation in data analytics. If you\u2019re brand new to the field, try out our <a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/short-courses\/become-a-data-analyst\/\">free, introductory data analytics short course<\/a>. And, for further reading, check out the following:<\/p>\n<ul>\n<li><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-science-vs-data-analytics-vs-machine-learning\/\">What\u2019s the difference between data analytics, data science, and machine learning?<\/a><\/li>\n<li><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/data-analyst-to-data-scientist-career-transition\/\">How to make the transition from data analyst to data scientist<\/a><\/li>\n<li><a href=\"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/machine-learning-interview-questions\/\">The most-asked machine learning interview questions (and answers!)<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning is a complex yet exciting field, and programming languages have a key role to play. So what&#8217;s the best language for machine learning? Find out here.<\/p>\n","protected":false},"author":101,"featured_media":634,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_lmt_disableupdate":"yes","_lmt_disable":"","footnotes":""},"categories":[3],"tags":[],"class_list":["post-3813","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analytics"],"acf":{"homepage_category_featured":false,"cards_inner_programs_lists_left":"","cards_inner_programs_lists_right":"","related_plan_cards":""},"modified_by":"Rash SEO","_links":{"self":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/3813","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/users\/101"}],"replies":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/comments?post=3813"}],"version-history":[{"count":10,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/3813\/revisions"}],"predecessor-version":[{"id":26002,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/posts\/3813\/revisions\/26002"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media\/634"}],"wp:attachment":[{"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/media?parent=3813"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/categories?post=3813"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/careerfoundry.inbearbeitung.de\/en\/wp-json\/wp\/v2\/tags?post=3813"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}