Pattern Recognition How is it different from Machine Learning

Pattern Recognition How is it different from Machine Learning Pattern Recognition : How is it different from Machine Learning Back Home Categories Online Courses Mock Interviews Webinars NEW Community Write for Us Categories Artificial Intelligence AI vs Machine Learning vs Deep LearningMachine Learning AlgorithmsArtificial Intelligence TutorialWhat is Deep LearningDeep Learning TutorialInstall TensorFlowDeep Learning with PythonBackpropagationTensorFlow TutorialConvolutional Neural Network TutorialVIEW ALL BI and Visualization What is TableauTableau TutorialTableau Interview QuestionsWhat is InformaticaInformatica Interview QuestionsPower BI TutorialPower BI Interview QuestionsOLTP vs OLAPQlikView TutorialAdvanced Excel Formulas TutorialVIEW ALL Big Data What is HadoopHadoop ArchitectureHadoop TutorialHadoop Interview QuestionsHadoop EcosystemData Science vs Big Data vs Data AnalyticsWhat is Big DataMapReduce TutorialPig TutorialSpark TutorialSpark Interview QuestionsBig Data TutorialHive TutorialVIEW ALL Blockchain Blockchain TutorialWhat is BlockchainHyperledger FabricWhat Is EthereumEthereum TutorialB lockchain ApplicationsSolidity TutorialBlockchain ProgrammingHow Blockchain WorksVIEW ALL Cloud Computing What is AWSAWS TutorialAWS CertificationAzure Interview QuestionsAzure TutorialWhat Is Cloud ComputingWhat Is SalesforceIoT TutorialSalesforce TutorialSalesforce Interview QuestionsVIEW ALL Cyber Security Cloud SecurityWhat is CryptographyNmap TutorialSQL Injection AttacksHow To Install Kali LinuxHow to become an Ethical Hacker?Footprinting in Ethical HackingNetwork Scanning for Ethical HackingARP SpoofingApplication SecurityVIEW ALL Data Science Python Pandas TutorialWhat is Machine LearningMachine Learning TutorialMachine Learning ProjectsMachine Learning Interview QuestionsWhat Is Data ScienceSAS TutorialR TutorialData Science ProjectsHow to become a data scientistData Science Interview QuestionsData Scientist SalaryVIEW ALL Data Warehousing and ETL What is Data WarehouseDimension Table in Data WarehousingData Warehousing Interview QuestionsData warehouse architectureTalend T utorialTalend ETL ToolTalend Interview QuestionsFact Table and its TypesInformatica TransformationsInformatica TutorialVIEW ALL Databases What is MySQLMySQL Data TypesSQL JoinsSQL Data TypesWhat is MongoDBMongoDB Interview QuestionsMySQL TutorialSQL Interview QuestionsSQL CommandsMySQL Interview QuestionsVIEW ALL DevOps What is DevOpsDevOps vs AgileDevOps ToolsDevOps TutorialHow To Become A DevOps EngineerDevOps Interview QuestionsWhat Is DockerDocker TutorialDocker Interview QuestionsWhat Is ChefWhat Is KubernetesKubernetes TutorialVIEW ALL Front End Web Development What is JavaScript â€" All You Need To Know About JavaScriptJavaScript TutorialJavaScript Interview QuestionsJavaScript FrameworksAngular TutorialAngular Interview QuestionsWhat is REST API?React TutorialReact vs AngularjQuery TutorialNode TutorialReact Interview QuestionsVIEW ALL Mobile Development Android TutorialAndroid Interview QuestionsAndroid ArchitectureAndroid SQLite DatabaseProgramming Frameworks you need to knowAI vs Machine Learning vs Deep LearningA Comprehensive Guide To Artificial Intelligence With Python Introduction to Deep Learning What is Deep Learning? 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The industry of Machine Learning is surely booming and in a good direction. Following pointers will be covered in this article:IntroductionMachine LearningPattern RecognitionFeatures of Pattern RecognitionTrainin g and Learning Models in Pattern RecognitionApplications of Pattern RecognitionAdvantages of Pattern RecognitionDifference Between Machine Learning and Pattern RecognitionSummaryIntroductionIn todays world, a lot of different type of data is flowing across systems in order to categorize the data we cannot use traditional programming which has rules that can check some conditions and classify data.The solution to this problem is Machine Learning, with the help of it we can create a model which can classify different patterns from data. One of the applications of this is the classification of spam or non-spam data.Machine LearningIn Machine Learning we cannot expect a model to be 100% accurate but the predictions should be as close as possible so that it can be categorized in a particular category. In Machine Learning the model is created based on some algorithms which learn from the data provided to make predictions.The model builds on statistics. Machine learning takes some data to analyze it and automatically create some model which can predict things. In order to get good predictions from a model, we need to provide data that has different characteristics so that the algorithms will understand different patterns which may exist in a given problem.Pattern RecognitionPatterns are recognized by the help of algorithms used in Machine Learning. Recognizing patterns is the process of classifying the data based on the model that is created by training data, which then detects patterns and characteristics from the patterns.Pattern recognition is the process which can detect different categories and get information about particular data. Some of the applications of patterns recognition are voice recognition, weather forecast, object detection in images, etc.Features of Pattern Recognition:Pattern recognition learns from the data.Automatically recognize patterns even when partially visible.Should be able to recognize patterns which are familiar.The pattern should be r ecognized from different angles and shapes.Training and Learning Models in Pattern RecognitionFirstly the data should be divided into to set i.e training and testing set. Learning from the data can tell how the predictions of the system are depending on the data provided as well which algorithm suits well for specific data, this is a very important phase. As data is divided into two categories we can use training data to train an algorithm and testing data is used to test model, as already said the data should be diverse training and testing data should be different.So we divide data into two sets normally we divide data in which 70% of data is used for training the model, algorithms extract the important patterns from the provided data and creates a model. Testing set contains 30% of whole data and it is then used to verify the performance of the model i.e how accurately is the model predicting the results.Applications of Pattern RecognitionComputer vision: Objects in images can be recognized with the help of pattern recognition which can extract certain patterns from image or video which can be used in face recognition, farming tech, etc.Civil administration: surveillance and traffic analysis systems to identify objects such as a car.Engineering: Speech recognition is widely used in systems such as Alexa, Siri, and Google Now.Geology: Rocks recognition, it helps geologist to detect rocks.Speech Recognition: In speech recognition, words are treated as a pattern and is widely used in the speech recognition algorithm.Fingerprint Scanning: In fingerprint recognition, pattern recognition is widely used to identify a person one of the application to track attendance in organizations.Advantages of Pattern RecognitionDNA sequences can be interpretedExtensively applied in the medical field and robotics.Classification problems can be solved using pattern recognition.Biometric detectionCan recognize a particular object from different angles.Difference Between Machine L earning and Pattern RecognitionML is an aspect which learns from the data without explicitly programmed, which may be iterative in nature and becomes accurate as it keeps performing tasks. ML is a form of pattern recognition which is basically the idea of training machines to recognize patterns and apply them to practical problems. ML is a feature which can learn from data and iteratively keep updating itself to perform better but, Pattern recognition does not learn problems but, it can be coded to learn patterns. Pattern recognition is defined as data classification based on the statistical information gained from patterns.Pattern recognition plays an important role in the task which machine learning is trying to achieve. Similarly, as humans learn by recognizing patterns. Patterns vary from visual patterns, sound patterns, signals, weather data, etc. ML model can be developed to understand patterns using statistical analysis which can classify data further. The results might be a probable value or depend on the likelihood of the occurrence of data.SummaryIn this article, we took a look at what is machine learning and pattern recognition, how they work together in order to create an accurate and efficient model. We explored different features of pattern recognition. Also, how the data is divided into a training set and testing set and how that can be used to create an efficient model which could provide accurate predictions. What are the applications of them and how they differ from each other is discussed in brief?Edurekas Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised LearningandNatural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.If you have any queries related to this article please leave them in thecomments sectionbelow and we w ill revert as soon as possible.Recommended videos for you Introduction to Mahout Watch Now Deep Learning Tutorial Deep Learning With TensorFlow Watch Now What Is Deep Learning Deep Learning Simplified Watch NowRecommended blogs for you What are the Advantages and Disadvantages of Artificial Intelligence? Read Article Recurrent Neural Networks (RNN) Tutorial | Analyzing Sequential Data Using TensorFlow In Python Read Article Artificial Intelligence What It Is And How Is It Useful? 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