{"id":991,"date":"2018-11-20T03:52:51","date_gmt":"2018-11-20T00:52:51","guid":{"rendered":"http:\/\/kusuaks7\/?p=596"},"modified":"2021-05-27T12:44:37","modified_gmt":"2021-05-27T12:44:37","slug":"learning-ai-if-you-suck-at-math-part-two-practical-projects","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/learning-ai-if-you-suck-at-math-part-two-practical-projects\/","title":{"rendered":"Learning AI if You Suck at Math\u200a\u2014\u200aPart Two\u200a\u2014\u200aPractical Projects"},"content":{"rendered":"<p><strong><em>Ready to learn Machine Learning? Browse<\/em><\/strong> <strong><em><a href=\"https:\/\/www.experfy.com\/training\/tracks\/machine-learning-training-certification\">Machine Learning Training and Certification courses<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n<p><strong><em>\u00a0<\/em><\/strong><\/p>\n<p id=\"6868\">If you read\u00a0the <a href=\"https:\/\/www.experfy.com\/blog\/learning-ai-if-you-suck-at-math-part-1\">first article in this series<\/a>, you\u2019re already on your way to upping your math game. Maybe some of those funny little symbols are starting to make sense.<\/p>\n<p id=\"a408\">But here\u2019s another dirty little secret nobody tells you about AI:<\/p>\n<blockquote id=\"1629\"><p>You don\u2019t actually need that much math to get\u00a0started.<\/p><\/blockquote>\n<p id=\"828c\">If you\u2019re a developer or sys-admin you probably already use a lot of libraries and frameworks that you know little about. You don\u2019t have to understand the inner workings of web-scraping to use\u00a0<a href=\"https:\/\/curl.haxx.se\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/curl.haxx.se\/\" data->curl<\/a>. The same is true with AI. There are a number of frameworks and projects that make it easy to get going fast without needing a data science Ph.D.<\/p>\n<p id=\"97ab\">Don\u2019t get me wrong. The math helps you feel confident about what\u2019s going on behind the scenes. It allows you to read research papers and advanced books like Ian Goodfellow\u2019s\u00a0<a href=\"http:\/\/amzn.to\/2jQ1dvY\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/amzn.to\/2jQ1dvY\" data->Deep Learning<\/a>\u00a0without your eyes glazing over. So keep studying the books I gave you in the last article. But if you want to start\u00a0<em>using\u00a0\u00a0<\/em>AI, you can do that today.<\/p>\n<p id=\"83f1\">Let\u2019s get started with some practical projects.<\/p>\n<figure id=\"eeae\" data-scroll=\"native\"><canvas width=\"48\" height=\"75\"><\/canvas><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*iB1_anM-rQkM9We4eAz30g.jpeg\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*iB1_anM-rQkM9We4eAz30g.jpeg\" \/><\/figure>\n<p id=\"b8f0\"><strong>My approach to learning is very similar to the excellent approach outlined in\u00a0<\/strong><a href=\"http:\/\/amzn.to\/2k5iQVS\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/amzn.to\/2k5iQVS\" data-><strong>The First Twenty Hours<\/strong><\/a><strong>.<\/strong>\u00a0We all know the 10,000 hour rule. To truly master a skill you need to put in a lot of time. But we\u2019re not there yet. We\u2019re just getting started. Right now we\u2019re trying to get from \u201cthis sucks\u201d to \u201cthis is so much fun!\u201d<\/p>\n<p id=\"cc82\">The basics of the approach are simple:<\/p>\n<ul>\n<li id=\"acda\">Pick a project.<\/li>\n<li id=\"1ceb\">Get past self-crippling beliefs.<\/li>\n<li id=\"3d99\">Try lots of stuff and fail fast.<\/li>\n<li id=\"2b7c\">Practice.<\/li>\n<\/ul>\n<p id=\"e760\">Easy, right? So let\u2019s go!<\/p>\n<h3 id=\"66c3\">Pick a\u00a0Project<\/h3>\n<p id=\"2b78\">First off, you need a project that will really motivate you to get out of your comfort zone.<\/p>\n<figure id=\"7c8d\" data-scroll=\"native\"><canvas width=\"75\" height=\"45\"><\/canvas><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*rSd5lQYgvIh0yzPSJmIaYw.png\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*rSd5lQYgvIh0yzPSJmIaYw.png\" \/><\/figure>\n<p id=\"2a0e\"><strong>How does\u00a0<\/strong><a href=\"https:\/\/www.kaggle.com\/c\/data-science-bowl-2017\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.kaggle.com\/c\/data-science-bowl-2017\" data-><strong>a project with a prize of one million dollars and a chance to make an impact on lung cancer research sound<\/strong><\/a><strong>?<\/strong><\/p>\n<p id=\"15e0\">Kaggle is\u00a0<em>the<\/em>\u00a0place for machine learning. Right now they\u2019re hosting a contest with a $1 million purse to improve classification of lung cancer lesions. Anyone can enter, including you.<\/p>\n<p id=\"64a6\">Now I know what you\u2019re thinking. There\u2019s no chance that I win this. This is a contest for heavy hitters. Glad you brought that up, because that brings us to step two:<\/p>\n<h3 id=\"df1b\"><strong>Get Over Self-Crippling Beliefs<\/strong><\/h3>\n<p id=\"d112\">The most important step in learning anything new is to shut down that little voice of self-doubt in your head as fast as possible. The First Twenty Hours advocates removing distractions, practicing on the clock and a number of other techniques. Throw in meditation, affirmations or heavy drinking. Whatever works. Just do whatever it takes to get that voice to go away so you can focus.\u00a0<strong>If you need a self-help book to get over the hump, try\u00a0<\/strong><a href=\"http:\/\/amzn.to\/2jGyuY1\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/amzn.to\/2jGyuY1\" data-><strong>You Are a Badass<\/strong><\/a><strong>, a fun, funny, sarcastic masterpiece!<\/strong><\/p>\n<figure id=\"bab1\" data-scroll=\"native\"><canvas width=\"50\" height=\"75\"><\/canvas><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*KCut1Ti85g0mrAWCVy2OLw.jpeg\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*KCut1Ti85g0mrAWCVy2OLw.jpeg\" \/><\/figure>\n<p id=\"91d8\">Here\u2019s the deal: You do suck right now. But that\u2019s OK! You won\u2019t for long.<\/p>\n<p id=\"6fe7\">Feeling confused and frustrated is always the first stage of learning. So rather than beating yourself up, see it as a sign that you\u2019re on the right track. You\u2019re learning something awesome!<\/p>\n<p id=\"9369\"><strong>You probably won\u2019t win the competition, but so what? Focus on getting a\u00a0competent entry submitted before the deadline.\u00a0<\/strong>Not everyone can win a marathon, but\u00a0<em>finishing one<\/em>\u00a0is a hell of an accomplishment in and of itself, right?<\/p>\n<p id=\"7dd4\"><strong>And you know what? You just might win. Seriously.<\/strong><\/p>\n<p id=\"df3c\">As an amateur, you\u2019re not burdened by years of theory and ideas that weigh down the professionals. Just remember the story about\u00a0<a href=\"http:\/\/www.snopes.com\/college\/homework\/unsolvable.asp\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/www.snopes.com\/college\/homework\/unsolvable.asp\" data->the student who solved two unsolvable math problems after finding them on the blackboard and mistaking them for homework assignments<\/a>.\u00a0The truth is data science is more art than science.\u00a0It\u2019s a field that attracts polymaths with all kinds of eclectic backgrounds. So get in there and try stuff.<\/p>\n<p id=\"72ef\">Who knows what will happen?<\/p>\n<p id=\"8864\">Maybe you\u2019ll see something the experts missed, make a real impact on cancer detection and take home some serious money to boot!<\/p>\n<h3 id=\"6ef9\">Try Lots of Stuff and Fail\u00a0Fast<\/h3>\n<p id=\"1f30\">If you\u2019re a dev-ops guy, you know this mantra. It applies to learning too. What I do is grab a bunch of samples of books and start skimming them quickly to see which ones make the most sense to me. Each person has a different style, so some books will work for one person and not another. Pick the one that works best for you.<\/p>\n<figure id=\"32dd\" data-scroll=\"native\"><canvas width=\"57\" height=\"75\"><\/canvas><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*PgByZLqqGrf7lL83x0HHWQ.jpeg\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*PgByZLqqGrf7lL83x0HHWQ.jpeg\" \/><\/figure>\n<p id=\"e992\"><strong>There are a few books on machine learning out there, like\u00a0<\/strong><a href=\"http:\/\/amzn.to\/2j2oAhl\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/amzn.to\/2j2oAhl\" data-><strong>Real World Machine Learning<\/strong><\/a><strong>.<\/strong>\u00a0Unfortunately, because the field is so new, most of the books are just starting to come out this year.\u00a0<strong>You can pre-order\u00a0<\/strong><a href=\"https:\/\/www.amazon.com\/Deep-Learning-Practitioners-Josh-Patterson\/dp\/1491914254\/ref=sr_1_2?ie=UTF8&amp;qid=1485122533&amp;sr=8-2&amp;keywords=deep+learning\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.amazon.com\/Deep-Learning-Practitioners-Josh-Patterson\/dp\/1491914254\/ref=sr_1_2?ie=UTF8&amp;qid=1485122533&amp;sr=8-2&amp;keywords=deep+learning\" data-><strong>Deep Learning: A Practitioners Approach<\/strong><\/a><strong>\u00a0or<\/strong><a href=\"http:\/\/amzn.to\/2j2k9mD\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/amzn.to\/2j2k9mD\" data-><strong>Hands-on Machine Learning with Scikit-Learn and Tensorflow<\/strong><\/a><strong>.<\/strong><\/p>\n<p id=\"bba1\">But you don\u2019t have to wait.\u00a0Let me introduce you to my friend safari<a href=\"https:\/\/www.safaribooksonline.com\/home\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.safaribooksonline.com\/home\/\" data-><strong> Books Online<\/strong><\/a><strong>.\u00a0<\/strong>For forty bucks a month you can read as many books as you want and you get access to books in progress\u00a0<em>before they\u2019re released<\/em>, including the two listed above.<\/p>\n<p id=\"d6c3\">I\u2019m going to save you some time though. Right now it\u2019s totally unnecessary to learn how to code deep learning systems from scratch in Python, R, or Java.\u00a0<strong>You need tools to get you rolling with Deep Learning fast so you can start working on your contest entry.<\/strong><\/p>\n<p id=\"fe71\"><strong>You want\u00a0<\/strong><a href=\"https:\/\/keras.io\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/keras.io\/\" data-><strong>Keras<\/strong><\/a><strong>\u00a0with either\u00a0<\/strong><a href=\"https:\/\/www.tensorflow.org\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.tensorflow.org\/\" data-><strong>TensorFlow<\/strong><\/a><strong>\u00a0or\u00a0<\/strong><a href=\"http:\/\/deeplearning.net\/software\/theano\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/deeplearning.net\/software\/theano\/\" data-><strong>Theano<\/strong><\/a><strong>.<\/strong><\/p>\n<figure id=\"180b\" data-scroll=\"native\"><canvas width=\"75\" height=\"30\"><\/canvas><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*LBjjmGofNJ_6vetddYAvIQ.jpeg\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*LBjjmGofNJ_6vetddYAvIQ.jpeg\" \/><\/figure>\n<p id=\"cc80\">You don\u2019t even need to set it up yourself.\u00a0<strong>Grab\u00a0<\/strong><a href=\"https:\/\/github.com\/saiprashanths\/dl-docker\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/github.com\/saiprashanths\/dl-docker\" data-><strong>this sweet all-in-one deep learning Docker image<\/strong><\/a><strong>.<\/strong><\/p>\n<p id=\"124a\">Frankly, it doesn\u2019t matter whether you use TensorFlow or Theano. They\u2019re basically engines for running machine learning. At this point in your education, both are equal, so pick one.<\/p>\n<p id=\"7a0a\"><strong>Keras is a library of machine learning frameworks created by a top notch Google AI researcher.<\/strong>\u00a0I had the good fortune of meeting the creator of Keras this weekend,\u00a0<a href=\"https:\/\/twitter.com\/fchollet?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/twitter.com\/fchollet?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor\" data->Francois Chollet<\/a>. He described Keras as the key to \u201cdemocratizing AI.\u201d He said that \u201cdeep learning is mature but it\u2019s not yet widely disseminated\u2026You don\u2019t have to be an AI researcher to use Keras.\u201d Instead, you can just start playing around with all kinds of state of the art algorithms right away.<\/p>\n<p id=\"7692\">If you\u2019ve already got a Mac or Linux rig with a good Nvidia graphics card you\u2019re good to go. If you don\u2019t, considering picking up an Alienware. I recommend the mid-range\u00a0<a href=\"http:\/\/www.dell.com\/en-us\/shop\/productdetails\/alienware-aurora-r6-desktop\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/www.dell.com\/en-us\/shop\/productdetails\/alienware-aurora-r6-desktop\" data->Aurora<\/a>\u00a0series. You don\u2019t need a kick-ass processor. You need an SSD, a secondary spinning disk to dump data to, 16\u201364GB of memory and the best Nvidia card(s) you can afford. Focus all your cash on the cards, as they really accelerate deep learning. You\u2019ll need to reformat it with Linux and get the latest binary drivers. Unfortunately, the open source ones won\u2019t cut it for the latest chipsets. They\u2019ll likely boot to a black screen.\u00a0<a href=\"http:\/\/askubuntu.com\/questions\/760934\/graphics-issues-after-while-installing-ubuntu-16-04-16-10-with-nvidia-graphics\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/askubuntu.com\/questions\/760934\/graphics-issues-after-while-installing-ubuntu-16-04-16-10-with-nvidia-graphics\" data->Fix that like this.<\/a><\/p>\n<p id=\"54f8\">There are also\u00a0<a href=\"https:\/\/medium.com\/@acrosson\/building-a-deep-learning-box-d17d97e2905c#.fp1xfgukz\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/medium.com\/@acrosson\/building-a-deep-learning-box-d17d97e2905c#.fp1xfgukz\" data->some tutorials<\/a>\u00a0<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2016\/11\/building-a-machine-learning-deep-learning-workstation-for-under-5000\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.analyticsvidhya.com\/blog\/2016\/11\/building-a-machine-learning-deep-learning-workstation-for-under-5000\/\" data->out there<\/a>\u00a0for building your own rig if you\u2019re a do-it-yourselfer. Also, I just added\u00a0<a href=\"https:\/\/hackernoon.com\/learning-ai-if-you-suck-at-math-p3-building-an-ai-dream-machine-or-budget-friendly-special-d5a3023140ef#.wktve8ouw\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/hackernoon.com\/learning-ai-if-you-suck-at-math-p3-building-an-ai-dream-machine-or-budget-friendly-special-d5a3023140ef#.wktve8ouw\" data->my own tutorial in part three<\/a>!<\/p>\n<p id=\"5d8b\">Lastly, you can use the\u00a0<a href=\"https:\/\/aws.amazon.com\/hpc\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/aws.amazon.com\/hpc\/\" data->AWS<\/a>,\u00a0<a href=\"https:\/\/cloud.google.com\/gpu\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/cloud.google.com\/gpu\/\" data->Google<\/a>\u00a0or\u00a0<a href=\"https:\/\/azure.microsoft.com\/en-us\/resources\/videos\/build-2016-introduction-to-nvidia-gpus-in-azure\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/azure.microsoft.com\/en-us\/resources\/videos\/build-2016-introduction-to-nvidia-gpus-in-azure\/\" data->Azure<\/a>\u00a0cloud, but GPU compute in the cloud can get expensive quick. Better to buy than lease until you know what you\u2019re doing.<\/p>\n<h3 id=\"ac33\"><strong>Practice<\/strong><\/h3>\n<p id=\"052c\">Now you\u2019re ready to get started. Here\u2019s\u00a0<a href=\"http:\/\/machinelearningmastery.com\/tutorial-first-neural-network-python-keras\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/machinelearningmastery.com\/tutorial-first-neural-network-python-keras\/\" data->a super-simple example for getting started with Keras<\/a>.<\/p>\n<p id=\"a8ee\"><strong>You are going to need an approach to the competition. Once again, I\u2019m going to save you some time.<\/strong><\/p>\n<p id=\"c0a6\"><strong>The most effective method of tagging and studying images at the moment is known as a\u00a0<\/strong><a href=\"https:\/\/adeshpande3.github.io\/adeshpande3.github.io\/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks\/\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/adeshpande3.github.io\/adeshpande3.github.io\/A-Beginner&#039;s-Guide-To-Understanding-Convolutional-Neural-Networks\/\"><strong>convolutional neural net<\/strong><\/a><strong>\u00a0(CNN).<\/strong>\u00a0Google, Facebook, Pinterest, and Amazon all use them for image processing and tagging. You might as well start with the best-practice, right?<\/p>\n<figure id=\"5f05\" data-scroll=\"native\"><canvas width=\"62\" height=\"75\"><\/canvas><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*hZ4qADYaUpyF1zTBG7kUsg.jpeg\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/600\/1*hZ4qADYaUpyF1zTBG7kUsg.jpeg\" \/><\/figure>\n<p id=\"318a\">In fact, if you head over the competition itself,\u00a0<a href=\"https:\/\/www.kaggle.com\/c\/data-science-bowl-2017\/data\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.kaggle.com\/c\/data-science-bowl-2017\/data\" data->get the data set<\/a>, and\u00a0<a href=\"https:\/\/www.kaggle.com\/c\/data-science-bowl-2017\/details\/tutorial\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.kaggle.com\/c\/data-science-bowl-2017\/details\/tutorial\" data->check out the tutorial<\/a>, you\u2019ll see that it walks you through slicing and dicing the images and using a CNN with Keras and TensorFlow backend. Voila!<\/p>\n<p id=\"cf67\">Frankly, you could do a lot worse than just implementing the tutorial and messing around with the parameters for a few weeks to see what you get.<\/p>\n<p id=\"8a95\">After that get crazy. Throw different parameters and algorithms at it. Experiment and have fun. Maybe you\u2019ll stumble across something the experts missed!<\/p>\n<p id=\"ba96\">If you\u2019re ready to try something more advanced after that, there are some great posts on\u00a0<a href=\"https:\/\/www.kaggle.com\/c\/data-science-bowl-2017\/kernels\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.kaggle.com\/c\/data-science-bowl-2017\/kernels\" data->the Kaggle Data Science Bowl 2017 board<\/a>. Turns out data scientists are not above sharing some of their secret sauce.\u00a0<a href=\"https:\/\/www.kaggle.com\/anokas\/data-science-bowl-2017\/exploratory-data-analysis\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.kaggle.com\/anokas\/data-science-bowl-2017\/exploratory-data-analysis\" data->Check out this one, which helps you start exploring the data<\/a>, which is a series of anonymized CT scans.<\/p>\n<p id=\"214f\"><a href=\"https:\/\/www.kaggle.com\/gzuidhof\/data-science-bowl-2017\/full-preprocessing-tutorial\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.kaggle.com\/gzuidhof\/data-science-bowl-2017\/full-preprocessing-tutorial\" data->This one is more advanced<\/a>\u00a0and currently the most popular post on the board for good reason. It helps you do \u201cpre-processing,\u201d which is basically scrubbing and massaging the data to make it easier for neural nets to deal with more fluidly. It actually turns the 2D images into 3D images! Super cool!<\/p>\n<p id=\"8035\">Frankly, if you type out all this code yourself and get it running, you\u2019re already kicking serious ass. This approach to programming is called \u201cthe hard way,\u201d i.e., just type in the code without thinking about it until you understand it. There is even\u00a0<a href=\"http:\/\/amzn.to\/2jQfHvR\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"http:\/\/amzn.to\/2jQfHvR\" data->a series of books on Python and other languages<\/a>\u00a0that take this approach to learning, and it may work for you.<\/p>\n<p id=\"2067\">One warning: Someone posted a perfect score in the competition already. He did it in a clever way,\u00a0<a href=\"https:\/\/www.kaggle.com\/olegtrott\/data-science-bowl-2017\/the-perfect-score-script\" target=\"_blank\" rel=\"noopener noreferrer\" data-href=\"https:\/\/www.kaggle.com\/olegtrott\/data-science-bowl-2017\/the-perfect-score-script\" data->by studying the leaderboards and effectively doubling his training set size<\/a>. It\u2019s perfectly legal, but it won\u2019t really help your goal, which is to learn about how to run neural nets against a training set for a good cause. I\u2019d skip this approach for now, and focus on running Keras against the CT scans.<\/p>\n<p id=\"813a\">That\u2019s it! With any luck, you\u2019ll help redefine cancer research and take home some cash too. Not a bad day\u2019s work.<\/p>\n<p id=\"807f\">But even if you don\u2019t win, you\u2019ll be well on your way to learning how to use AI in the real world.<\/p>\n<p id=\"5894\">Whatever happens, remember to have fun!<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>If you&rsquo;re a developer or sys-admin you probably already use a lot of libraries and frameworks that you know little about. You don&rsquo;t have to understand the inner workings of web-scraping to use curl. The same is true with AI. There are a number of frameworks and projects that make it easy to get going fast without needing a data science Ph.D. The math helps you feel confident about what&rsquo;s going on behind the scenes. If you want to start&nbsp;using AI, you can do that today. Let&rsquo;s get started with some practical projects.<\/p>\n","protected":false},"author":393,"featured_media":24211,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-post-2.php","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[2209],"class_list":["post-991","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":2209,"user_id":393,"is_guest":0,"slug":"daniel-jeffries","display_name":"Daniel Jeffries","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Jeffries","first_name":"Daniel","job_title":"","description":"Dan Jeffries is an author, engineer and serial entrepreneur. During his two decades in the computer industry, he&#039;s covered a broad range of tech from Linux to networks and virtualization.&nbsp;"}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/991","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\/393"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=991"}],"version-history":[{"count":2,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/991\/revisions"}],"predecessor-version":[{"id":24213,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/991\/revisions\/24213"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/24211"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=991"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=991"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=991"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=991"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}