{"id":622,"date":"2018-03-13T02:26:49","date_gmt":"2018-03-12T23:26:49","guid":{"rendered":"http:\/\/kusuaks7\/?p=227"},"modified":"2025-08-27T12:16:54","modified_gmt":"2025-08-27T12:16:54","slug":"machine-learning-why-it-matters","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/machine-learning-why-it-matters\/","title":{"rendered":"Machine Learning: Why it Matters?"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\nAre you into Machine Learning OR are you \u201cjust\u201d a Statistician? Have you been asked this question yet? If you are in a career or looking to get into one that has anything to do with deriving insights out of data, you probably know what I am talking about.\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThe year 2016 has seen over three dozen machine learning startups being acquired by tech giants; another several dozen machine learning startups raked up a aggregate funding to the tune of $4 Billion worldwide. Is it a blip or a bubble? Definitely not. In times when automation is key, it was but imperative that we figure out methods of data analysis & model building that automates data analysis & model building. Sounds tautological? It is. And in a way that is what machine learning is \u2026 err \u2026 rather does. It picks up right where traditional statistical models stop. It\u2019s all about building algorithms that learn iteratively from data. The more data you feed it, the better results it churns out.\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tWhile conceptually machine learning has been around for more than 80 years (recent history dates it back to World War II and Turing), the recent frenzy around it can be attributed to the overall advances and affordability in computing power. While manually getting these models to improve themselves through numerous iterations may seem tedious, if not impossible, a modern computer fed with the algorithm can get these models to learn, grow, change, and develop by themselves in a matter of seconds \u2026 and we are already talking \u201creal-time!\u201d What more, they can look for insights without being told exactly where to look for insights a.k.a. dealing with unstructured data (think social media, web-searches). It iterates, learns new stuff, and adapts, and iterates and continues the whole process all over again learning from new data every time.\u00a0It really embodies the adage that practice makes perfect.\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tNow if you put this in the context of the self-driving cars, or the recommender engines in Netflix or Amazon \u2013 you can see why such algorithms that generate decisions out of data real time without human intervention, would be key to where we are headed both in terms of technology and user experience.\u00a0 It is machine learning that has turned the \u201chype\u201d around the importance of \u201cbig data\u201d into a reality. When availability of more data could have caused concerns around it\u2019s usability for deriving meaningful insights, it was machine learning that came to the rescue. Let\u2019s just say that compared to traditional statistical methods which dealt with static models, machine learning is more in tune with the current times and it\u2019s needs.\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tThe discussion becomes a bit more exciting and a little more tangible when we start considering some problems where machine learning is a clear improvement over traditional statistical methods (although a strong caveat here would be \u2026 a lot of machine learning techniques are really enhancements or extensions of their \u201cstatistical\u201d counterparts).\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tLet\u2019s start with\u00a0Pattern Recognition: it is the essence of solving a lot of business problems that rely on regularities in the gathered data to make predictions. It is also what is called \u201csupervised learning\u201d in machine learning parlance. While a traditional classification and regression model can give you a prediction, it is a \u201cclosed-form\u201d solution which of course is static. A machine learning technique called gradient boosting takes the same approach but iteratively and continuously searches for a \u201clocal minimum\u201d and adapts as it learns. Ever had your credit card declined when using it a gas station that\u2019s not your \u201cusual\u201d one? In most cases that is supervised (or semi-supervised) learning models at work for you.\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tBut given supervised learning deals prominently with \u201chistorical data\u201d or a \u201ctraining set,\u201d it has to live with it\u2019s own limitations of being unable to predict in situations where you have no past data to predict the future. While the traditional k-means or hierarchical clustering can in theory be applied, when you are dealing with a deluge of transactional data, you need to apply unsupervised learning techniques like ANN or GMM to explore the surpassed data and find structure within it. So the next time you see the \u201cAlso recommended for you\u201d while shopping on Amazon.com, know that every new item you searched, every new item you saved, and every new item you bought, were factored into those recommendations by those unsupervised machine learning algorithms within a matter of a few seconds.\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tIf you are dealing with discrete outcome variables, logistic regression techniques naturally come to mind, but fall short when it comes to dealing with complex data sets \u2013 and hence the need for looking into Support Vector Regressions or Hierarchical Bayesian models. If you are looking at a large but well-behaved data set the good ol\u2019 logit will still work great; but with the big bad ugly ones you probably have to resort to the much talked about Bagged Regression technique, which by the way is nothing more than a thousand logits estimated through random sample draws from the mother data set, which are then averaged to minimize bias and variance.\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tOf course, we are often looking at blurred lines when applying supervised, semi-supervised, and unsupervised learning techniques to business problems. When you first open an YouTube account, the recommended content served on your home page has to be based on in-session click-stream based unsupervised models. With repeat visits, as the system learns the depth and spread of your viewing preference, semi-supervised dynamic models with temporal aspects akin to Hidden-Markov-Models probably kick-in. (I am not commenting on what algorithms YouTube actually applies to these problems \u2013 rather I am stating what could potentially be applied)\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

In conclusion\u2026<\/h4><\/h4>\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tOne way to view machine learning is as though it were statistics on steroids: keep building thousands of trees with simpler assumptions and then average them up to derive predictions yielding lower bias and variance. \u00a0Machine learning is concerned\u00a0more by the accuracy of final predictions rather than the laundry list of underlying distributions and\u00a0asymptotic tests in statistical methods. That doesn\u2019t necessarily mean that the math is not complex \u2013 it just says that the intent is much simpler to understand.\u00a0Contrary to the common myth, all machine learning techniques are NOT adaptive (actually only the generative algorithms are). And no, you will not be expected to dive into the deep-end of writing algorithms which require \u201creal time\u201d on Day 1.\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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tOriginally appeared in InsideBIGDATA<\/a>\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":"

Ready to learn Machine Learning? Browse courses\u00a0like\u00a0Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. Are you into Machine Learning OR are you \u201cjust\u201d a Statistician? Have you been asked this question yet? If you are in a career or looking to get into one that has anything<\/p>\n","protected":false},"author":253,"featured_media":3405,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[1773],"class_list":["post-622","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":1773,"user_id":253,"is_guest":0,"slug":"smita-adhikary","display_name":"Smita Adhikary","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Adhikary","first_name":"Smita","job_title":"","description":" Smita Adhikary is Managing Consultant at Big Data Analytics Hires<\/a>,  a talent search and recruiting firm focused primarily on Data Science and Decision Science professionals. She has spent many years in management consulting and built statistical models for banks and credit card companies."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/622","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\/253"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=622"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/622\/revisions"}],"predecessor-version":[{"id":38089,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/622\/revisions\/38089"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3405"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=622"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=622"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=622"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=622"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}