[{"data":1,"prerenderedAt":77},["ShallowReactive",2],{"technologies":3,"blog:ai-evolution-done-right-with-these-15-machine-learning-techniques:":7},[4],{"slug":5,"label":6},"php","PHP",{"id":8,"source":9,"title":10,"slug":11,"url":12,"excerpt":13,"image":14,"author":15,"date":16,"date_formatted":17,"categories":18,"tags":22,"content":23,"seo":24,"related":25},22425,"wordpress","AI Evolution Done Right with these 15 Machine Learning Techniques ","ai-evolution-done-right-with-these-15-machine-learning-techniques","\u002Fai-evolution-done-right-with-these-15-machine-learning-techniques","Machine Learning Techniques: The domain of artificial intelligence (AI) is dynamic and is expanding constantly. The growth of AI is incessant because just like...","https:\u002F\u002Fyugasa.com\u002Fpublic\u002Fwp-content\u002Fuploads\u002F2021\u002F09\u002FAI-Evolution-Done-Right-with-these-15-Machine-Learning-Techniques-.jpg","Creative Team","2021-09-09T13:28:20+00:00","September 9, 2021",[19],{"name":20,"slug":21},"AI & Chatbots","ai-chatbots",[],"\u003Cspan style=\"font-weight: 400\">Machine Learning Techniques: The domain of \u003Cstrong>\u003Ca href=\"https:\u002F\u002Ftest.yugasa.org\u002Fartificial-intelligence\u002F\">artificial intelligence (AI)\u003C\u002Fa>\u003C\u002Fstrong> is dynamic and is expanding constantly. The growth of AI is incessant because just like humans AI undergoes a rigorous process of learning and adaptivity. When machines learn, adapt, and grow, the phenomenon is known as machine learning. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Machine learning is a significant branch of AI that deals with the logistics of data and algorithms by imitating the learning process of humans. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">One of the primary purposes of machine learning is to give meaning to complex data. It deals with huge sets of data daily that is impossible for the human workforce to handle. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Having said that, one should know that machine learning today has occupied a significant space in the sphere of information and technology. Some of the industries where machine learning is dominant are healthcare, education, media, finance, retail, and manufacturing. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Effective and innovative customer and consumer management is unimaginable without machine learning integration.  \u003C\u002Fspan>\r\n\u003Ch2>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Learning the Basics of Machine Learning Techniques\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh2>\r\n\u003Cspan style=\"font-weight: 400\">Know that owning a fair idea and knowledge about the know-how of machine learning is the key to the positive growth of your enterprise. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Let us start with the basics because knowing the basics well will help you to explore more of the machine learning techniques. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Stick with the blog. \u003C\u002Fspan>\r\n\u003Ch2>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">The Five Crucial Components of Machine Learning \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh2>\r\n\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Data Set: \u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan style=\"font-weight: 400\">We know that machines function on data. Machines also learn from data. In machine learning, huge sets of data are dealt with. The larger the sets of data you feed the machine with, the better are the chances for the machine learning model to learn and be trained well. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Make sure your data set consists of these five characteristics: \u003C\u002Fspan>\r\n\r\n\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Volume:\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan style=\"font-weight: 400\">\u003Cstrong>\u003Cspan style=\"color: #15a3bc\"> \u003C\u002Fspan>\u003C\u002Fstrong>remember that the scalability of the data you are about to feed your machine learning model is what matters the most. As mentioned already, the larger the data set, the better it gets for the machine learning model to learn and arrive at optimal decisions. \u003C\u002Fspan>\r\n\r\n\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Diversity:\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan style=\"font-weight: 400\"> Data can be of various types. It can be in the form of texts, images, videos, and even cryptic texts that humans cannot decipher. In some cases, data can even border on absurdity and such data are known as complex data. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">The more variety in data fed to the model, the better are the chances for it to learn. Make your machine learning model accustomed to every kind of data under the sun so that no time is wasted later. \u003C\u002Fspan>\r\n\r\n\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Velocity: \u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan style=\"font-weight: 400\">One of the main purposes to integrate machine learning in any infrastructure is to ensure the speedy yielding of results in a short period. The speed at which the model accumulates data matters. \u003C\u002Fspan>\r\n\r\n\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Value:\u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan style=\"font-weight: 400\">\u003Cstrong>\u003Cspan style=\"color: #15a3bc\"> \u003C\u002Fspan>\u003C\u002Fstrong>The data that the model takes in should have value. No matter how big or complex the data set is, it should be outright meaningful. Feeding meaningless data to the machine learning model will yield meaningless results that can also obscure the process of decision-making. \u003C\u002Fspan>\r\n\r\n\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Veracity: \u003C\u002Fspan>\u003C\u002Fstrong>\u003Cspan style=\"font-weight: 400\">While feeding data to your machine learning model, remember to check the accuracy of data. Inaccuracy in data can give inaccurate output. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Algorithms \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Machine learning is all about algorithms. Algorithms are a logical program that turns a data set into a model. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">A machine learns with the help of these algorithms. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Models \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">A model, in machine learning, is a computational representation of real-world processes. A machine learning model is rigorously trained to identify and recognize these real-world patterns just as humans do through cognitive learning. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Once a model is trained well enough to identify and recognize patterns, it will be able to make predictions and decisions quickly. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Extracting Feature \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Feature extraction is the process of making reductions in the number of features in a dataset by creating new features from the features that are existing. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">This technique is crucial to the proper training of a machine learning model because data sets come with multiple features. Too much variety in features can be overwhelming for the machine learning model to learn. The ML model will start suffering from overfitting. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">The problem of overfitting takes place when an ML model starts learning the details and the noise in the data in its training period to such an extent that it starts impacting the data and its results negatively.  \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Training \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Training of an ML model involves readying it for the market. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">We shall explore it in the upcoming sections of this article. \u003C\u002Fspan>\r\n\u003Ch2>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Diving Deep into the Machine Learning Techniques \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh2>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Regression Analyses \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Regression analysis is a modeling technique that is used for prediction. This modeling technique aims to build a relationship between the dependent (target) and independent (predictor). \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">A model that is created using this ML technique is the dynamicity of dependent variables which are in correspondence with the independent variables. In this way, the regression analysis modeling technique can make predictions. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">This ML technique is most popular in the healthcare industry that predicts blood pressure and suppressed medical symptoms in patients. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Classification\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Classification is a technique of categorizing data into several classes. The process of classification includes recognizing and grouping ideas and objects into categories. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">There are seven ways to classify machine learning data sets: \u003C\u002Fspan>\r\n\u003Cul>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Logistic regression \u003C\u002Fspan>\u003C\u002Fli>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">K-nearest neighbors \u003C\u002Fspan>\u003C\u002Fli>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Random forest \u003C\u002Fspan>\u003C\u002Fli>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Decision tree \u003C\u002Fspan>\u003C\u002Fli>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Stochastic gradient descent \u003C\u002Fspan>\u003C\u002Fli>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Naïve Bayes \u003C\u002Fspan>\u003C\u002Fli>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Support Vector machine \u003C\u002Fspan>\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Transfer Learning \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Transfer learning is an ML modeling technique that is used on data sets that are already trained and will perform similar tasks. In this process, layers of trained data set are transferred and combine with the layers of a new data set so that the machine learning algorithms can recognize the new task. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">The technique of transfer learning is pocket-friendly in terms of computational resources. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Clustering - Machine Learning Techniques\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">In the clustering method of learning, observations are grouped or are formed into clusters. The observations that are grouped should be of similar characteristics. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">It trains the ML model in such a way that the algorithms are required to define the output instead of just delivering them. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Clustering is famously used in anomaly detection, face detection, medical imaging, market segmentation, and social network analysis. Defining the output helps to maintain credibility thereby justifying the results delivered. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Ensemble Method - Machine Learning Techniques\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">The ensemble method, as the name suggests, brings various predictive models in one place to deliver one precise output. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">The ensemble method of modeling reduces the chances of bias which an individual machine learning model runs the risk of. Multiple predictive models help to balance the precision quality. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Reduction in Dimensionality \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">High-dimensional data occupies a lot of space. This is where dimensionality reduction comes into the picture. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Dimensionality reduction is the technique of data representation that scales down the data into low-dimensional space. This helps to make complex data simple in the ML model. Additionally, this will take up less computation time. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Dimensional reduction technique is applied in noise reduction, data visualization etc. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Deep learning and Neural Networks \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Intertwined at its core, the phrase, “neural network” has been taken from biology that causes the brain to observe and learn. Neural networks installed in machines serve the same purpose. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Deep learning is a set of techniques that makes the neural networks learn and re-learn and imitate the human brain. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">These two terms are the famous terms in the domain of AI that are widely used in medical imaging, image classification, video mapping, etc. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Natural Language Processing \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Natural language processing (NLP) is the technique that teaches machines to understand and comprehend verbal actions such as words and texts like humans. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">NLP is largely used in every industry as it is reliable for reducing complexities and produce outputs with clear meaning. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">These are the common applications of NLP: \u003C\u002Fspan>\r\n\u003Cul>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Text prediction \u003C\u002Fspan>\u003C\u002Fli>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Emotions and sentiment analysis \u003C\u002Fspan>\u003C\u002Fli>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Speech recognition \u003C\u002Fspan>\u003C\u002Fli>\r\n \t\u003Cli>\u003Cspan style=\"font-weight: 400\">Natural language generation \u003C\u002Fspan>\u003C\u002Fli>\r\n\u003C\u002Ful>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Reinforcement Learning \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Machines, through reinforcement learning, can cope up with new situations by the virtue of the training they receive and the learning they gather. The data sets are often absent in reinforcement models and the machines tend to learn by experience. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Word Embeddings \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">A word embedding is a learned representation of texts where words with the same meaning will have similar representations. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">For example, words like the flower rose, the fragrance will be represented similarly. \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Action Analysis - Machine Learning Techniques\u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">All actions in the action analysis are carried out by two techniques. The outcomes produced are fed into the machine learning memory.  \u003C\u002Fspan>\r\n\u003Ch3>\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Decision Trees \u003C\u002Fspan>\u003C\u002Fstrong>\u003C\u002Fh3>\r\n\u003Cspan style=\"font-weight: 400\">Do you remember drawing family trees during your school days? A decision tree is something exactly like that. It is a flowchart-like structure. In this, each internal node represents a test on a feature. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">The decision tree modeling technique includes knowledge management platforms and is widely used for machine learning, product planning, etc. \u003C\u002Fspan>\r\n\r\n\u003Cstrong>\u003Cspan style=\"color: #15a3bc\">Who all is it all meant for? \u003C\u002Fspan>\u003C\u002Fstrong>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Now that we know pretty much about the basics and the different types of machine learning techniques, data scientists need to pay keen attention to all the details in the piece. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Data scientists are responsible for performing machine learning modeling so that the desired objectives like efficient customer management, analyzing marketing behavior and much more are attained. \u003C\u002Fspan>\r\n\r\n\u003Cspan style=\"font-weight: 400\">Any data scientist who can master every nook and cranny of machine learning can help the AI domain to evolve which will result in the evolution of businesses as well. \u003C\u002Fspan>\r\n\r\nRead More:\r\n\u003Cul>\r\n \t\u003Cli class=\"entry-title\">\u003Ca href=\"https:\u002F\u002Ftest.yugasa.org\u002Fai-chatbots\u002Fwill-ai-artificial-intelligence-replace-software-developers\u002F\">WILL AI (ARTIFICIAL INTELLIGENCE) REPLACE SOFTWARE DEVELOPERS?\u003C\u002Fa>\u003C\u002Fli>\r\n \t\u003Cli class=\"entry-title\">\u003Ca href=\"https:\u002F\u002Ftest.yugasa.org\u002Fai-chatbots\u002Fai-vs-ml-what-is-the-difference-between-them-in-2021\u002F\">ARTIFICIAL INTELLIGENCE VS. MACHINE LEARNING: WHAT IS THE DIFFERENCE BETWEEN THEM?\u003C\u002Fa>\u003C\u002Fli>\r\n\u003C\u002Ful>",{"title":10,"description":13,"image":14},[26,44,55,66],{"id":27,"source":28,"title":29,"slug":30,"url":31,"excerpt":32,"image":33,"author":34,"date":35,"date_formatted":36,"categories":37,"tags":43},232,"laravel","How to Build a Real-Time Fan Engagement Platform for Sports and Stadium Experiences","how-to-build-a-real-time-fan-engagement-platform-for-sports-and-stadium-experiences","\u002Fblog\u002Fhow-to-build-a-real-time-fan-engagement-platform-for-sports-and-stadium-experiences","Learn how to build a sports fan engagement platform with real-time data, venue integrations, AI workflows and secure stadium operations.","https:\u002F\u002Fadmin.yugasa.com\u002Fuploads\u002Fhow-to-build-a-real-time-fan-engagement-platform-for-sports-and-stadium-experiences.png","Admin","2026-09-15T00:00:00+00:00","September 15, 2026",[38,40],{"name":39,"slug":21},"AI Chatbots",{"name":41,"slug":42},"Artificial Intelligence","artificial-intelligence",[],{"id":45,"source":28,"title":46,"slug":47,"url":48,"excerpt":49,"image":50,"author":34,"date":35,"date_formatted":36,"categories":51,"tags":54},233,"How to Scale a Mobile Learning Platform Across Learners, Teachers and Training Centres","how-to-scale-a-mobile-learning-platform-across-learners-teachers-and-training-centres","\u002Fblog\u002Fhow-to-scale-a-mobile-learning-platform-across-learners-teachers-and-training-centres","Learn how to build a resilient learning platform with multi-tenant data, offline mobile access, automation and secure enterprise operations.","https:\u002F\u002Fadmin.yugasa.com\u002Fuploads\u002Fhow-to-scale-a-mobile-learning-platform-across-learners-teachers-and-training-centres.png",[52,53],{"name":39,"slug":21},{"name":41,"slug":42},[],{"id":56,"source":28,"title":57,"slug":58,"url":59,"excerpt":60,"image":61,"author":34,"date":35,"date_formatted":36,"categories":62,"tags":65},234,"How to Preserve Customer, Order and Financial Data During Platform Migration","how-to-preserve-customer-order-and-financial-data-during-platform-migration","\u002Fblog\u002Fhow-to-preserve-customer-order-and-financial-data-during-platform-migration","Build this approach to protect records, preserve integrity and reduce cutover risk.","https:\u002F\u002Fadmin.yugasa.com\u002Fuploads\u002Fhow-to-preserve-customer-order-and-financial-data-during-platform-migration.png",[63,64],{"name":39,"slug":21},{"name":41,"slug":42},[],{"id":67,"source":28,"title":68,"slug":69,"url":70,"excerpt":71,"image":72,"author":34,"date":35,"date_formatted":36,"categories":73,"tags":76},235,"A Practical Guide to Migrating Legacy Software to a Modern Architecture","a-practical-guide-to-migrating-legacy-software-to-a-modern-architecture","\u002Fblog\u002Fa-practical-guide-to-migrating-legacy-software-to-a-modern-architecture","Learn how these services reduce migration risk through discovery, phased architecture, testing and specialist engineering support.","https:\u002F\u002Fadmin.yugasa.com\u002Fuploads\u002Fa-practical-guide-to-migrating-legacy-software-to-a-modern-architecture.png",[74,75],{"name":39,"slug":21},{"name":41,"slug":42},[],1789543817151]