{"id":598,"date":"2018-02-06T00:43:21","date_gmt":"2018-02-05T21:43:21","guid":{"rendered":"http:\/\/kusuaks7\/?p=203"},"modified":"2025-05-15T16:05:22","modified_gmt":"2025-05-15T16:05:22","slug":"demystifying-machine-learning-how-do-machines-really-learn","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/demystifying-machine-learning-how-do-machines-really-learn\/","title":{"rendered":"Demystifying machine learning: How do machines really learn?"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tReady to learn Machine Learning? Browse courses<\/a>\u00a0like\u00a0Machine Learning Foundations: Supervised Learning<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIt seems that everybody is talking about\u00a0artificial intelligence<\/a>\u00a0and machine learning these days. But machine learning is not a new concept \u2014 it\u2019s actually been around since the\u00a0late 1950s<\/a>. So why all the hype? Why do businesses feel like now is the time to adopt? Before businesses start to develop a strategy around machine learning and AI, it\u2019s important to review how machines really learn, and how this can impact your AI and machine learning strategies.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nTo start, there\u2019s been a lot of discussion around the difference between AI and machine learning, and even the term analytics has become very nebulous. What\u2019s important from a business perspective is not the technical definitions, but the value it brings you. All of these technologies are powered by data, and the goal for a business is simply to do what it takes to move from data, to insight to outcomes as efficiently as possible.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tOne area where there is a clear connection between data, insight and outcomes is in the field of maintenance. People have long relied on routine maintenance as an attempt to ward off potential problems. Until recently, we changed our oil every 5,000 miles. Not because there was an indication of failure, but because we were too reliant on routine. In short, we didn\u2019t have the data to tell us whether we\u00a0needed<\/em>\u00a0to change the oil. In this case, it wasn\u2019t a major problem since it\u2019s fairly inexpensive to get your oil changed, but imagine if you had thousands of machines or extremely expensive machines? Taking a single expensive machine offline, like an aircraft engine, when it\u2019s not necessary or managing thousands of machines using a static maintenance routine are both problematic. Read: You\u2019re wasting money when you\u2019re making maintenance changes based on routine. Instead, businesses need to start using data to move away from routine maintenance, which will help save money in the long run.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe large volume of data presents a tremendous opportunity, but it can be hard to take full advantage. After all, you don\u2019t simply need more data to\u00a0guide your decisions<\/a>\u00a0\u2014 you\u2019re likely drowning in that already. In fact, one of the biggest questions that you may be facing as a business leader is how to utilize the data you\u2019re already generating on a daily basis.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tPerhaps you\u2019re already\u00a0employing data scientists<\/a>\u00a0to build the models you need to make sense of it all, but they can only process a fraction of the data that is flooding in. Hiring more data scientists is not only expensive, but there is such a shortage that hiring enough may be a logistical impossibility. A better solution is to utilize a machine learning platform to remove this burden from the team processing your data, allowing them to focus on making decisions while the platform focuses on analysis at scale.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAs we covered above,\u00a0more data<\/a>\u00a0is readily available than ever before. Beyond the IoT sensors that constantly generate machine data, there\u2019s also behavior data from customers and users, and business transaction data in an ERP or CRM itching to be analyzed. Data is in such abundance that the level of contextual insight that can be derived is phenomenal.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tHumans, however, have a hard time processing data at this level. The average human is capable of processing several dimensions at once \u2014 but when too many variables are introduced, even the most intelligent among us can\u2019t process it all. We start eliminating variables, or we generalize, which reduces the accuracy of our analysis.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tMachines, on the other hand, do not have this limitation. Computers boil everything down to binary decisions \u2014 yes or no states, 0s and 1s \u2014 and eliminate nothing. Mathematical models can then be designed to simulate real-world behaviors, and the computer can run the entirety of the data input against the model to yield a result that is as accurate as possible.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe key then to producing accurate results lies in the model. Ordinarily the creation \u2014 and critically, the frequent tuning \u2014 of a model is a complex task, requiring the painstaking labor of a team of trained data scientists. To keep the model up to date, it must take in the very latest and most relevant data, and the new analysis must be compared with past analysis and results, leading the data scientist to a series of conclusions that allow them to further increase the accuracy. This can be a difficult and time-consuming process.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFortunately, it\u2019s all math, and we can use machine learning to automate much of this process \u2014 I know it sounds strange, but we are basically using machine learning to automate the machine learning process! Why rely on data scientists to do the manual work of running and deciphering which\u00a0set of algorithms<\/a>\u00a0provides the best prediction? A computer can automate the testing of the models without limiting the tests to a subset of the data. And a computer can do this continuously in order to respond to operational or environmental changes. While it appears as if the machine is learning, it is merely improving the accuracy of the models that will generate the most accurate result. This approach allows you to use your data scientists to analyze the outcomes versus spending all their time doing routine work. Or to enable the average business user (like me) to make sense of the predictions, even when we don\u2019t understand the math. So, while there\u2019s no actual \u201clearning,\u201d the magic is in the math, which is now available to the average organization.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Machine learning is not a new concept — it’s actually been around since the late 1950s. So,  why all the hype? Why do businesses feel like now is the time to adopt? Before businesses start to develop a strategy around machine learning and AI, it’s important to review how machines really learn, and how this can impact your AI and machine learning strategies.<\/p>\n","protected":false},"author":200,"featured_media":3286,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[1715],"class_list":["post-598","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":1715,"user_id":200,"is_guest":0,"slug":"mark-troester","display_name":"Mark Troester","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Troester","first_name":"Mark","job_title":"","description":"Mark Troester is the Vice President of Strategy at Progress<\/a><\/strong>. He has extensive experience in bringing application development and big data products to market. Previously, he led product marketing efforts at Sonatype, SAS and Progress DataDirect."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/598","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/200"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=598"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/598\/revisions"}],"predecessor-version":[{"id":37803,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/598\/revisions\/37803"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3286"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=598"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=598"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=598"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=598"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}