[{"data":1,"prerenderedAt":75},["ShallowReactive",2],{"i-material-symbols:language":3,"i-material-symbols:person":8,"i-mdi:instagram":10,"i-mdi:youtube":12,"i-ri:linkedin-fill":14,"i-ri:twitter-x-line":16,"i-ri:facebook-fill":18,"post-basics-of-machine-learning":20,"i-material-symbols:person-outline":69,"i-material-symbols:calendar-month":71,"i-mdi:schedule":73},{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":7},0,24,false,"\u003Cpath fill=\"currentColor\" d=\"M8.125 21.213q-1.825-.788-3.187-2.15t-2.15-3.188T2 11.988t.788-3.875t2.15-3.175t3.187-2.15T12.013 2t3.875.788t3.175 2.15t2.15 3.175t.787 3.875t-.787 3.887t-2.15 3.188t-3.175 2.15t-3.875.787t-3.888-.787M12 19.95q.65-.9 1.125-1.875T13.9 16h-3.8q.3 1.1.775 2.075T12 19.95m-2.6-.4q-.45-.825-.787-1.713T8.05 16H5.1q.725 1.25 1.813 2.175T9.4 19.55m5.2 0q1.4-.45 2.488-1.375T18.9 16h-2.95q-.225.95-.562 1.838T14.6 19.55M4.25 14h3.4q-.075-.5-.112-.987T7.5 12t.038-1.012T7.65 10h-3.4q-.125.5-.187.988T4 12t.063 1.013t.187.987m5.4 0h4.7q.075-.5.113-.987T14.5 12t-.038-1.012T14.35 10h-4.7q-.075.5-.112.988T9.5 12t.038 1.013t.112.987m6.7 0h3.4q.125-.5.188-.987T20 12t-.062-1.012T19.75 10h-3.4q.075.5.113.988T16.5 12t-.038 1.013t-.112.987m-.4-6h2.95q-.725-1.25-1.812-2.175T14.6 4.45q.45.825.788 1.713T15.95 8M10.1 8h3.8q-.3-1.1-.775-2.075T12 4.05q-.65.9-1.125 1.875T10.1 8m-5 0h2.95q.225-.95.563-1.838T9.4 4.45Q8 4.9 6.912 5.825T5.1 8\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":9},"\u003Cpath fill=\"currentColor\" d=\"M9.175 10.825Q8 9.65 8 8t1.175-2.825T12 4t2.825 1.175T16 8t-1.175 2.825T12 12t-2.825-1.175M4 20v-2.8q0-.85.438-1.562T5.6 14.55q1.55-.775 3.15-1.162T12 13t3.25.388t3.15 1.162q.725.375 1.163 1.088T20 17.2V20z\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":11},"\u003Cpath fill=\"currentColor\" d=\"M7.8 2h8.4C19.4 2 22 4.6 22 7.8v8.4a5.8 5.8 0 0 1-5.8 5.8H7.8C4.6 22 2 19.4 2 16.2V7.8A5.8 5.8 0 0 1 7.8 2m-.2 2A3.6 3.6 0 0 0 4 7.6v8.8C4 18.39 5.61 20 7.6 20h8.8a3.6 3.6 0 0 0 3.6-3.6V7.6C20 5.61 18.39 4 16.4 4zm9.65 1.5a1.25 1.25 0 0 1 1.25 1.25A1.25 1.25 0 0 1 17.25 8A1.25 1.25 0 0 1 16 6.75a1.25 1.25 0 0 1 1.25-1.25M12 7a5 5 0 0 1 5 5a5 5 0 0 1-5 5a5 5 0 0 1-5-5a5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3a3 3 0 0 0 3 3a3 3 0 0 0 3-3a3 3 0 0 0-3-3\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":13},"\u003Cpath fill=\"currentColor\" d=\"m10 15l5.19-3L10 9zm11.56-7.83c.13.47.22 1.1.28 1.9c.07.8.1 1.49.1 2.09L22 12c0 2.19-.16 3.8-.44 4.83c-.25.9-.83 1.48-1.73 1.73c-.47.13-1.33.22-2.65.28c-1.3.07-2.49.1-3.59.1L12 19c-4.19 0-6.8-.16-7.83-.44c-.9-.25-1.48-.83-1.73-1.73c-.13-.47-.22-1.1-.28-1.9c-.07-.8-.1-1.49-.1-2.09L2 12c0-2.19.16-3.8.44-4.83c.25-.9.83-1.48 1.73-1.73c.47-.13 1.33-.22 2.65-.28c1.3-.07 2.49-.1 3.59-.1L12 5c4.19 0 6.8.16 7.83.44c.9.25 1.48.83 1.73 1.73\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":15},"\u003Cpath fill=\"currentColor\" d=\"M6.94 5a2 2 0 1 1-4-.002a2 2 0 0 1 4 .002M7 8.48H3V21h4zm6.32 0H9.34V21h3.94v-6.57c0-3.66 4.77-4 4.77 0V21H22v-7.93c0-6.17-7.06-5.94-8.72-2.91z\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":17},"\u003Cpath fill=\"currentColor\" d=\"M10.488 14.651L15.25 21h7l-7.858-10.478L20.93 3h-2.65l-5.117 5.886L8.75 3h-7l7.51 10.015L2.32 21h2.65zM16.25 19L5.75 5h2l10.5 14z\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":19},"\u003Cpath fill=\"currentColor\" d=\"M14 13.5h2.5l1-4H14v-2c0-1.03 0-2 2-2h1.5V2.14c-.326-.043-1.557-.14-2.857-.14C11.928 2 10 3.657 10 6.7v2.8H7v4h3V22h4z\"\u002F>",{"postType":21,"id":22,"author":23,"date":26,"modified":27,"title":28,"slug":29,"seoTitle":30,"seoDescription":31,"excerpt":32,"trimmedExcerpt":33,"content":34,"readingTime":35,"featuredImage":36,"postCategory":42,"relatedPosts":47},"post",64303,{"id":24,"name":25},"author-0301142015","Canto","2024-02-16T14:51:25","2025-11-13T09:49:05","Machine learning 101: What ML is and how it works","basics-of-machine-learning","Unlocking ML: The basics of machine learning","Get a basic overview of what machine learning is, how ML works, and what industries are being impacted by ML and neural networks in 2024.","Once upon a time (and not that long ago), we thought the best way to solve a problem with software was to make an expert system. We’d find an expert and ask them a lot of questions, write up some rules, and then write a program to apply those rules in just the right order. […]","Once upon a time (and not that long ago), we thought the best way to solve a problem with software...","\u003Cp class=\"wp-block-paragraph\">Once upon a time (and not that long ago), we thought the best way to solve a problem with software was to make an \u003Cem>expert system\u003C\u002Fem>. We’d find an expert and ask them a lot of questions, write up some rules, and then write a program to apply those rules in just the right order. This approach encapsulates human knowledge in a very narrow way in a program.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Sometimes it worked, but other times we would leave out an important rule or use the wrong \u003Cem>expert\u003C\u002Fem>. These systems were static, meaning that they could not get any smarter or adjust if things changed.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Enter machine learning.\u003C\u002Fp>\u003Ch2 class=\"wp-block-heading\" id=\"toc-1--what-is-machine-learning-ml\">What is machine learning (ML)?\u003C\u002Fh2>\u003Cfigure class=\"wp-block-image aligncenter\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"880\" height=\"450\" src=\"\u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183947\u002Fai_machine_learning_venn_diagram.jpg\" alt=\"Venn diagram of AI and machine learning in green on an orange background.\" class=\"wp-image-64309\" srcset=\"\u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183947\u002Fai_machine_learning_venn_diagram.jpg 880w, \u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183947\u002Fai_machine_learning_venn_diagram-450x230.jpg 450w, \u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183947\u002Fai_machine_learning_venn_diagram-768x393.jpg 768w\" \u002F>\u003C\u002Ffigure>\u003Cp class=\"wp-block-paragraph\">Machine learning (ML) is the idea that if you have a large collection of data, instead of finding some expert to make up rules, you \u003Cem>let data teach the system\u003C\u002Fem> what is meaningful. Machine learning allows a computer to see patterns. You feed in some information you have, and the machine learns to return a correct prediction. If you keep feeding new data to the resulting computer, it keeps getting smarter. More data is great, and the more diverse the data, the better the model performs.\u003Cbr \u002F>\u003C\u002Fp>\u003Ch2 class=\"wp-block-heading\" id=\"toc-2--how-does-machine-learning-work\">How does machine learning work?\u003C\u002Fh2>\u003Cp class=\"wp-block-paragraph\">Machine learning uses things called neural networks, which are nodes in a program that act a bit like brain cells. These nodes get \u003Cem>excited\u003C\u002Fem> by specific things. The magic of machine learning is no one tells each of these nodes what to get excited about! The model itself decides what is important by looking repeatedly at large data examples to detect patterns and determine which ones matter.\u003C\u002Fp>\u003Ccto-card white=\"true\" shadow=\"false\">\u003Ch3 class=\"wp-block-heading\">“A machine learning model is a black box: we give it inputs and it generates the right output, but we are not sure what happens inside the box.”\u003C\u002Fh3>\u003C\u002Fcto-card>\u003Cp class=\"wp-block-paragraph\">There is a subset of machine learning called deep learning. The \u003Cem>deep\u003C\u002Fem> part refers to needing deeper neural networks (many more nodes in complicated formations). As you might guess, deep learning is used to tackle more complicated problems.\u003C\u002Fp>\u003Cfigure class=\"wp-block-image aligncenter\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"880\" height=\"450\" src=\"\u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183947\u002Fai_machine_learning_deep_learning_venn_diagram.jpg\" alt=\"Venn diagram of AI, machine learning, and deep learning in green on an orange background.\" class=\"wp-image-64308\" srcset=\"\u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183947\u002Fai_machine_learning_deep_learning_venn_diagram.jpg 880w, \u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183947\u002Fai_machine_learning_deep_learning_venn_diagram-450x230.jpg 450w, \u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183947\u002Fai_machine_learning_deep_learning_venn_diagram-768x393.jpg 768w\" \u002F>\u003C\u002Ffigure>\u003Cp class=\"wp-block-paragraph\">Let’s start with exploring non-deep machine learning and see what it can do!\u003C\u002Fp>\u003Ch2 class=\"wp-block-heading\" id=\"toc-3--machine-learning-use-cases\">Machine learning use cases\u003C\u002Fh2>\u003Cfigure class=\"wp-block-image aligncenter\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"880\" height=\"450\" src=\"\u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183948\u002Fmachine_learning_use_cases.jpg\" alt=\"Hovering icons for real estate, healthcare, email, e-commerce, financial, and product reviews over an orange background.\" class=\"wp-image-64307\" srcset=\"\u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183948\u002Fmachine_learning_use_cases.jpg 880w, \u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183948\u002Fmachine_learning_use_cases-450x230.jpg 450w, \u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183948\u002Fmachine_learning_use_cases-768x393.jpg 768w\" \u002F>\u003C\u002Ffigure>\u003Cp class=\"wp-block-paragraph\">What is machine learning good for in the real world? Here are some examples (and each uses a different flavor of machine learning) to help marketers, healthcare professionals, and finance professionals do their jobs faster and more effectively:\u003Cbr \u002F>\u003C\u002Fp>\u003Ch3 class=\"wp-block-heading\">1. Real estate value\u003C\u002Fh3>\u003Cp class=\"wp-block-paragraph\">You can predict house prices based on factors like size, location, and number of bedrooms. Linear regression models can analyze these variables to predict prices.\u003C\u002Fp>\u003Ch3 class=\"wp-block-heading\">2. Medical diagnosis based on symptoms\u003C\u002Fh3>\u003Cp class=\"wp-block-paragraph\">A decision tree can help doctors diagnose diseases by following a tree-like model of decisions and their possible consequences.\u003C\u002Fp>\u003Ch3 class=\"wp-block-heading\">3. Email spam detection\u003C\u002Fh3>\u003Cp class=\"wp-block-paragraph\">Support vector machines (SVMs) can classify emails as spam or legitimate by learning from the characteristics of known spam and non-spam emails. SVMs find how to best separate the different classes of data.\u003C\u002Fp>\u003Ch3 class=\"wp-block-heading\">4. Shopping recommendations\u003C\u002Fh3>\u003Cp class=\"wp-block-paragraph\">Customers’ purchase histories can be used to recommend products on an e-commerce site. K-nearest neighbors can suggest products that similar customers have bought. This technique looks at the ‘K’ closest points (customers with similar histories) and makes predictions based on their behaviors.\u003C\u002Fp>\u003Ch3 class=\"wp-block-heading\">5. Credit scoring\u003C\u002Fh3>\u003Cp class=\"wp-block-paragraph\">Logistic regression can be used to predict the probability of a customer defaulting on a loan. Logistic regression is used for binary (yes or no) classification problems (like default or no default).\u003C\u002Fp>\u003Ch3 class=\"wp-block-heading\">6. Sentiment analysis in product reviews\u003C\u002Fh3>\u003Cp class=\"wp-block-paragraph\">\u003Cem>Naïve Bayes classifiers\u003C\u002Fem> can classify reviews as positive, negative, or neutral.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">The more data you can feed a model, the better it can make its predictions, so living in the time of \u003Cem>big data\u003C\u002Fem> feeds right into machine learning.\u003C\u002Fp>\u003Ch2 class=\"wp-block-heading\" id=\"toc-4--machine-learning-and-gpus\">Machine learning and GPUs\u003C\u002Fh2>\u003Cp class=\"wp-block-paragraph\">Another thing that enables machine learning is \u003Cem>graphics processing unit (GPU) chips\u003C\u002Fem>, originally developed to help video games run quickly! Why, you might ask? It turns out that the math behind machine learning is all about very rapidly handling vectors.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Think of vectors as arrows pointing in a specific direction and of a specific length. This is exactly how the amazing screens in video games are generated.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">As video gamers have insisted on increasingly life-like visuals, the science community has benefitted from faster GPUs, as GPUs can act in parallel.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">It has been estimated that it would take 355 years to train ChatGPT on a single GPU, but because they used something like 25,000 GPUs in parallel, it only took a matter of days. Machine learning models that would take months (or longer) to train on traditional computer CPUs can happen in hours on fast GPUs.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Without gamers, we would still be years away from having ChatGPT.\u003C\u002Fp>\u003Ch2 class=\"wp-block-heading\" id=\"toc-5--can-machine-learning-solve-complex-problems\">Can machine learning solve complex problems?\u003C\u002Fh2>\u003Cp class=\"wp-block-paragraph\">The use cases above were previously impossible or very expensive to solve, but they either return a yes or no answer (is it spam or not? Will Bob default or not?).\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">There are way more complicated problems we need to solve, like getting computers to understand English, finding the right moment in a movie from a verbal description, or developing and training the recently famous ChatGPT.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">There is a subset of machine learning called \u003Ca href=\"\u002Fblog\u002Funcovering-the-mystery-of-deep-learning\u002F\">deep learning\u003C\u002Fa> that is better suited to tackling harder problems with far more complicated answers.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Deeper problems deserve deeper machine learning models!\u003C\u002Fp>",4,{"url":37,"url_md":38,"alt":39,"height":40,"width":41},"https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183946\u002Fbasics_of_machine_learning_feature.jpg","https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2024\u002F02\u002F19183946\u002Fbasics_of_machine_learning_feature-450x225.jpg","Machine analyzing patterns for prediction with icons of various industries over a green background.",600,1200,[43],{"id":44,"slug":44,"title":45,"path":46},"ai","Artificial Intelligence","\u002Fpost-category\u002Fai\u002F",[48,55,62],{"featuredImage":49,"postId":50,"slug":51,"title":52,"description":53,"path":54},"https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2020\u002F04\u002F19191912\u002Fai-advertising-450x311.jpg",44377,"ai-advertising","The most important ways AI is changing advertising","Artificial intelligence is a growing technological component that has unique benefits for companies. Check out how it's revolutionizing advertisements.","\u002Fblog\u002Fai-advertising\u002F",{"featuredImage":56,"postId":57,"slug":58,"title":59,"description":60,"path":61},"https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2023\u002F04\u002F19184132\u002Fai-content-creation-Feature-450x263.png",63506,"ai-content-creation","How to scale marketing programs using AI content creation","With AI content creation tools, marketers can quickly scale to deliver customized, personalized content to specific audiences to boost engagement.","\u002Fblog\u002Fai-content-creation\u002F",{"featuredImage":63,"postId":64,"slug":65,"title":66,"description":67,"path":68},"https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2025\u002F07\u002F28190035\u002Fhumanize-AI-content-feature-450x236.jpg",70728,"how-to-humanize-ai-content","How to humanize AI and why content authenticity always wins","Learn how to humanize AI content with real emotion and authenticity. Stand out in an AI-saturated world by boosting connection and brand trust.","\u002Fblog\u002Fhow-to-humanize-ai-content\u002F",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":70},"\u003Cpath fill=\"currentColor\" d=\"M9.175 10.825Q8 9.65 8 8t1.175-2.825T12 4t2.825 1.175T16 8t-1.175 2.825T12 12t-2.825-1.175M4 20v-2.8q0-.85.438-1.562T5.6 14.55q1.55-.775 3.15-1.162T12 13t3.25.388t3.15 1.162q.725.375 1.163 1.088T20 17.2V20zm2-2h12v-.8q0-.275-.137-.5t-.363-.35q-1.35-.675-2.725-1.012T12 15t-2.775.338T6.5 16.35q-.225.125-.363.35T6 17.2zm7.413-8.587Q14 8.825 14 8t-.587-1.412T12 6t-1.412.588T10 8t.588 1.413T12 10t1.413-.587M12 18\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":72},"\u003Cpath fill=\"currentColor\" d=\"M12 14q-.425 0-.712-.288T11 13t.288-.712T12 12t.713.288T13 13t-.288.713T12 14m-4.712-.288Q7 13.426 7 13t.288-.712T8 12t.713.288T9 13t-.288.713T8 14t-.712-.288M16 14q-.425 0-.712-.288T15 13t.288-.712T16 12t.713.288T17 13t-.288.713T16 14m-4 4q-.425 0-.712-.288T11 17t.288-.712T12 16t.713.288T13 17t-.288.713T12 18m-4.712-.288Q7 17.426 7 17t.288-.712T8 16t.713.288T9 17t-.288.713T8 18t-.712-.288M16 18q-.425 0-.712-.288T15 17t.288-.712T16 16t.713.288T17 17t-.288.713T16 18M5 22q-.825 0-1.412-.587T3 20V6q0-.825.588-1.412T5 4h1V2h2v2h8V2h2v2h1q.825 0 1.413.588T21 6v14q0 .825-.587 1.413T19 22zm0-2h14V10H5z\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":74},"\u003Cpath fill=\"currentColor\" d=\"M12 20a8 8 0 0 0 8-8a8 8 0 0 0-8-8a8 8 0 0 0-8 8a8 8 0 0 0 8 8m0-18a10 10 0 0 1 10 10a10 10 0 0 1-10 10C6.47 22 2 17.5 2 12A10 10 0 0 1 12 2m.5 5v5.25l4.5 2.67l-.75 1.23L11 13V7z\"\u002F>",1789382232407]