{"id":49788,"date":"2019-02-18T12:19:35","date_gmt":"2019-02-18T06:49:35","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=49788"},"modified":"2025-07-27T19:57:56","modified_gmt":"2025-07-27T14:27:56","slug":"machine-learning-infographic","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/machine-learning-infographic\/","title":{"rendered":"Machine Learning Infographic &#8211; Everything You Need to Know About ML"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:1418,&quot;href&quot;:&quot;https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Machine_learning&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251130072921\\\/https:\\\/\\\/en.wikipedia.org\\\/wiki\\\/Machine_learning&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 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19:54:58&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-14 02:25:23&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-17 05:49:08&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-20 06:38:49&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-23 08:15:10&quot;,&quot;http_code&quot;:404},{&quot;date&quot;:&quot;2026-04-26 10:02:48&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-04-29 14:13:55&quot;,&quot;http_code&quot;:429},{&quot;date&quot;:&quot;2026-05-02 19:39:01&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-06 04:50:24&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-09 06:14:41&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-12 08:20:37&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-15 09:29:22&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-18 11:00:28&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-21 13:05:16&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-24 13:13:21&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-27 13:51:19&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-30 15:11:03&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-02 18:48:44&quot;,&quot;http_code&quot;:404}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-02 18:48:44&quot;,&quot;http_code&quot;:404},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<h3>What is Machine Learning?<\/h3>\n<p>ML is the art of granting machines the ability to think. It is a field of computer science that uses statistical techniques to give machines the ability to learn without being explicitly programmed. Researchers claim this is a good way to observe progress toward human-level AI. This Machine Learning\u00a0Infographic is specially designed for beginners, covers all the <strong>basic concept of ML in an image form<\/strong>.<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorial\/\"><strong>Machine Learning<\/strong><\/a> deals with building algorithms that can receive input data, perform statistical analysis to predict output, and update the output as newer data become available.<\/p>\n<p><em>Machine Learning is &#8211;\u00a0Optimizing a performance criterion using example data and past experience. <strong>Alpavdin<\/strong><\/em><\/p>\n<p><em>Netflix saved $1 Billion in 2018 because of its Machine Learning algorithm. <strong>StatWolf<\/strong><\/em><\/p>\n<p><em>The revenue generated by AI software will grow from $1.4 billion in 2016 to $59.8 billion by 2025. <strong>Tractica\u00a0<\/strong><\/em><\/p>\n<h3>Machine Learning Infographic<\/h3>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/02\/Machine-Learning-Infographic-learn-ML.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-51076 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/02\/Machine-Learning-Infographic-learn-ML.jpg\" alt=\"Learn Machine Learning for beginners\" width=\"1200\" height=\"8062\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/02\/Machine-Learning-Infographic-learn-ML.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/02\/Machine-Learning-Infographic-learn-ML-768x5160.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/02\/Machine-Learning-Infographic-learn-ML-520x3494.jpg 520w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<h3>Kinds of Learning in ML<\/h3>\n<p>ML is a subset of <strong><a href=\"https:\/\/data-flair.training\/blogs\/artificial-intelligence-tutorial\/\">Artificial Intelligence (AI)<\/a><\/strong>. One part of the family of machine learning methods is Deep Learning. This is an approach to Machine Learning and is based on learning data representations (feature learning) instead of on task-specific algorithms.<\/p>\n<p>The three kinds of learning in Machine Learning are:<\/p>\n<ol>\n<li>Supervised Learning<\/li>\n<li>Unsupervised Learning<\/li>\n<li>Reinforcement Learning<\/li>\n<\/ol>\n<p>Other related forms of Machine Learning include semi-supervised and self-supervised learning methods. Semi-supervised learning involves using both labeled and unlabeled data to produce better results as compared to supervised learning. Self-supervised learning on the other hand involves creating labels for the data and this helps in minimizing the use of big labeled data set. These advanced approaches are further upgrading the functionalities and performances of the Machine Learning systems in numerous applications.<\/p>\n<h3>Steps in Machine Learning<\/h3>\n<p>Often, we carry out the following set of steps in Machine Learning:<\/p>\n<ol>\n<li>Collect data<\/li>\n<li>Filter data<\/li>\n<li>Analyze data<\/li>\n<li>Train algorithms<\/li>\n<li>Test algorithms<\/li>\n<li>Use algorithms for future predictions<\/li>\n<\/ol>\n<p>It is common to observe that Machine Learning models are updated via feedback cycles and assessment of performance. This process of continual updating minimizes over-fitting and allows the models to incorporate new information, hence providing more accurate prediction as the pattern changes with time. Furthermore, integration of the Machine Learning applications with real time data sources help in improving the application performance in terms of speed and precision in highly volatile situations.<\/p>\n<h3>Ethical Challenges\u00a0in Machine Learning<\/h3>\n<p>There are some ethical challenges that <strong><a href=\"https:\/\/en.wikipedia.org\/wiki\/Machine_learning\">Machine Learning<\/a><\/strong> presents:<\/p>\n<ul>\n<li>Digitization of cultural prejudices on use of systems trained on biased datasets (algorithmic bias)<\/li>\n<li>Training of machines on languages corpora that contain biases<\/li>\n<li>The possibility that these systems in the health care field might be designed not in public interest, but to boost income<\/li>\n<\/ul>\n<p>While Machine Learning can prove to be really effective in improving health care, these ethical questions must be addressed first.<\/p>\n<h3>Conclusion<\/h3>\n<p>Infographics help beginners learn faster by showing patterns clearly. For example, an ML lifecycle infographic can show steps like data collection, preprocessing, training, evaluation, and deployment. Another infographic might compare supervised and unsupervised learning using diagrams.<\/p>\n<p>Infographics are widely used in teaching, marketing, and blogs. They simplify tough topics, boost memory, and make learning machine learning more fun and clear.<\/p>\n<p>We hope this Machine Learning infographic could serve to intrigue you to consider a career in the domain. We have a complete <strong><a href=\"https:\/\/data-flair.training\/blogs\/machine-learning-tutorials-home\/\">series of Machine Learning tutorials<\/a><\/strong>. You may refer them to learn Machine Learning.\u00a0Keep reading and leave your comments and suggestions below.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What is Machine Learning? ML is the art of granting machines the ability to think. 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