{"id":1701,"date":"2020-09-18T13:33:25","date_gmt":"2020-09-18T13:33:25","guid":{"rendered":"https:\/\/data-science.gotoauthority.com\/2020\/09\/18\/courseras-machine-learning-for-everyone-fulfills-unmet-training-needs\/"},"modified":"2020-09-18T13:33:25","modified_gmt":"2020-09-18T13:33:25","slug":"courseras-machine-learning-for-everyone-fulfills-unmet-training-needs","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2020\/09\/18\/courseras-machine-learning-for-everyone-fulfills-unmet-training-needs\/","title":{"rendered":"Coursera\u2019s Machine Learning for Everyone Fulfills Unmet Training Needs"},"content":{"rendered":"<p>\n     Coursera&#8217;s Machine Learning for Everyone (free access) fulfills two different kinds of unmet learner needs, for both the technology side and the business side, covering state-of-the-art techniques, business leadership best practices, and a wide range of common pitfalls and how to avoid them.\n  <\/p>\n<div id=\"post-\">\n<p><span>Sponsored Post.<\/span><\/p>\n<div><a href=\"http:\/\/www.machinelearning.courses\/\" target=\"_blank\" rel=\"noopener noreferrer\"><img src=\"https:\/\/i.ibb.co\/4TVbjpM\/mach-lrng-800x425.jpg\" alt=\"Image\" width=\"100%\"><\/a><\/div>\n<p>\u00a0<\/p>\n<p><span><b>Unmet Training Needs Fulfilled by Coursera&#8217;s<br \/>\u201cMachine Learning for Everyone\u201d<\/b><\/span><\/p>\n<p>Coursera&#8217;s <a href=\"http:\/\/www.machinelearning.courses\/\" rel=\"noopener noreferrer\" target=\"_blank\">Machine Learning for Everyone<\/a> <b>(free access)<\/b> fulfills two different kinds of unmet learner needs. It\u2019s a conceptually-complete, end-to-end course series \u2013 its three courses amount to the equivalent of a college or graduate-level course \u2013 that covers both the technology side and the business side. While fully accessible and understandable to business-level learners, it\u2019s also also vital to data scientists and budding technical practitioners, since it covers:<\/p>\n<ul>\n<li>The state-of-the-art techniques\n<\/li>\n<li>The business leadership best practices\n<\/li>\n<li>A wide range of common pitfalls and how to avoid them\n<\/li>\n<\/ul>\n<div><img src=\"https:\/\/i.ibb.co\/J3qM4WR\/siegel-machine-learning-exciting-science-THUMBNAIL.jpg\" alt=\"Image\" width=\"60%\"><\/div>\n<p>\u00a0<\/p>\n<h3>\n<b>1) A comprehensive go-to for <\/b><b>BUSINESS-SIDE<\/b><b> learners \u2013 by covering the following:<\/b><br \/>\n<\/h3>\n<p>\u00a0<\/p>\n<ul>\n<li>ML project leadership (management process)\n<\/li>\n<li>ML algorithms: substantive yet accessible coverage\n<\/li>\n<li>Data preparation\n<\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<h3>\n<b>2) Need-to-knows for <\/b><b>EVERYONE<\/b><b> in ML \u2013 both business-side learners and technical practitioners \u2013 by also covering the following:<\/b><br \/>\n<\/h3>\n<p>\u00a0<\/p>\n<ul>\n<li>ML ethics: risks to social justice, equitable models, machine bias, etc.\n<\/li>\n<li>Business-oriented performance metrics\n<\/li>\n<li>Uplift modeling (aka persuasion modeling)\n<\/li>\n<li>Major pitfalls, in-depth:\n<ul>\n<li>P-hacking\n<\/li>\n<li>Overfitting\n<\/li>\n<li>The accuracy fallacy\n<\/li>\n<li>Presuming causation from correlations\n<\/li>\n<li>Serious problems with hyping ML as &#8220;AI&#8221;\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<h3><b>This checklist illustrates the unique contribution of this curriculum:<\/b><\/h3>\n<p>\u00a0<\/p>\n<div><img src=\"https:\/\/i.ibb.co\/7k8nbdH\/checklist.jpg\" alt=\"Image\" width=\"100%\"><\/div>\n<h3><b>More information about \u201cMachine Learning for Everyone\u201d:<\/b><\/h3>\n<p>\u00a0<\/p>\n<p>\u00a0<br \/><strong>Click here for free enrollment: <\/strong><strong><a href=\"http:\/\/www.machinelearning.courses\/\" rel=\"noopener noreferrer\" target=\"_blank\">Machine Learning for Everyone<\/a> <\/strong><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/www.kdnuggets.com\/2020\/09\/siegel2-coursera-machine-learning-training.html<\/p>\n","protected":false},"author":0,"featured_media":1702,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2],"tags":[],"_links":{"self":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/posts\/1701"}],"collection":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/comments?post=1701"}],"version-history":[{"count":0,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/posts\/1701\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/media\/1702"}],"wp:attachment":[{"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/media?parent=1701"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/categories?post=1701"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wealthrevelation.com\/data-science\/wp-json\/wp\/v2\/tags?post=1701"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}