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      "body": "<div class=\"text-justify\">\n\n\nHere are some interesting everyday examples around us where machines are learning and doing amazing jobs from detecting skin cancer to sorting cucumbers, to detecting escalators in need of repair, machine learning has granted computer systems entirely new abilities. E-mail spam recognition spell checks even the YouTube video recommendation is implemented using machine learning. \n\nMachine learning software is deeply embedded into the tech industry and is vital to most businesses. The global market size for machine learning is expected to reach a value of 117.19 billion by 2027 and who knows where it'll be after that. Some applications of machine learning are listed here.\n\n![](https://i.imgur.com/GVB5SyA.png)\n[ Figure: Applications of Machine Learning   (Edited by me on PowerPoint)]\n\n\n\n\n\n## Explanation:\n\n**In healthcare**, where Diagnostics are predicted for doctors, review the sentiment analysis that the tech giants are doing on social media.\n\n**In the finance sector**, another interesting application of machine learning for fraud detection.\n\n**In the e-commerce sector**, machine learning is used to predict customer churn while booking the gap you must have encountered surge pricing often where it says the Farrow field trip has been updated.\n\n**In transportation**, an interesting machine learning model is used by global taxi giant “uber” and others where they have differential pricing in real-time based on demand the number of cars available in case of bad weather or rush hour, etc.\n\n•\tSpeech recognition\n•\tVoice recognition\n•\tAutomated and self-driving Google car\n•\tSmart Homes\n•\tSmart Cities\n•\tSmart Agriculture\n•\tLearning Associations\n•\tMedical Diagnosis\n•\tClassification Processes\n•\tStatistical Arbitrage etc.\n\n## Summary\n\nIn a nutshell, Machine Learning amazingly integrates things in the field of artificial technology. Although it has some shocking effects when you think about it. But these machine learning applications are just one of the many ways in which this technology can improve our lives.\n\n\n\n\n\n![Divider 2.png](https://cdn.steemitimages.com/DQmcku5JTo92tt37Rv4CrLhKAvJSaVwmNsvRKWev3CDcPSc/Divider%202.png)\n\nI am also thankful to the management of SIZ for providing such a great platform where we can show our talent and skills.</div>\n\nCC: @cryptokraze @siz-official\n\n![Footer.png](https://cdn.steemitimages.com/DQmZC7bpcF5HuGVbgJu6KZDyRj3JrvcgCiay5mAsFSMyhU6/Footer.png)",
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      "body": "You have a great knowledge about machine learning. \nAnd i see many members make his posting on algorithms so, that's cause your post is also increase my knowledge",
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      "body": "<div class=\"text-justify\">\n\n\nWhat is machine learning at the science of our computers learn about being explicitly programmed it lies at the intersection of hacking skills and math and statistics without the need for substantive expertise that's because the machine learning algorithms don't need to know the data type that's being used for training they don't need to know for example that this is a financial or a medical data so where do you start in the next few slides we're going to cover some of the machine learning algorithms.\n\nExploration and research have highlighted some of the most popular classes or types of algorithms:\n\n1.\tFortson classification \n2.\tLogistic regression \n3.\tSupport vector\n4.\tRandom forests\n5.\tLeast-squares \n6.\tClustering detection etc.\n\n\n<center>![](https://i.imgur.com/YpxrK0A.jpg)</center>\n\n<center>[Figure_7: Machine Learning Algorithms  (Made by me)]</center>\n\n![Divider 2.png](https://cdn.steemitimages.com/DQmcku5JTo92tt37Rv4CrLhKAvJSaVwmNsvRKWev3CDcPSc/Divider%202.png)\n\n## Explanation:\n\n\t\n•\t**Logistic regression** is a linear model with fast training where the clusters are expressed in probabilities for example good or bad or 1 or 0. Regression is great for predicting a continuous-valued output.\n\n•\t**Support vector** is a very effective algorithm described as the best-supervised learning area. It’s more robust to outliers and handles non-linearity.\n\n•\t**Fortson classification** is great for text classification. It assumes independent variables for example in a case of a text it assumes that each Word contributes to the overall probability independently.\n\n•\t**Clustering** is an automating grouping of similar objects intercepts or clusters and is useful as an initial step in data exploration. \n\n•\t**Least-square**s is a multivariate method to find the best possible match for the given data sets. It constructs the SRF by using slope and constant values so that an analyst can find the micro scale possible value.\n\n•\t**Random vector** when working with multiple random variables, it is sometimes helpful to use vector and matrix displays. This makes the formulas more cohesive and allows us to apply facts from linear algebra.\n\n![Divider 2.png](https://cdn.steemitimages.com/DQmcku5JTo92tt37Rv4CrLhKAvJSaVwmNsvRKWev3CDcPSc/Divider%202.png)\n\nI am also thankful to the management of SIZ for providing such a great platform where we can show our talent and skills.</div>\n\nCC: @cryptokraze @siz-official\n\n![Footer.png](https://cdn.steemitimages.com/DQmZC7bpcF5HuGVbgJu6KZDyRj3JrvcgCiay5mAsFSMyhU6/Footer.png)",
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      "body": "<div class=\"text-justify\">\n\n\nThe term data processing refers to “a processing module that takes input, organize and calculate the data by applying various mathematical and logical operations on it to get its meaningful and valuable form”. A processing module for IoT data structures goes through multiple stages called processing life cycle.\n\n![](https://i.imgur.com/DVRF2om.png)\n Figure: Processing Life Cycle (Personally Edited on PowerPoint)\n\n![Divider 2.png](https://cdn.steemitimages.com/DQmcku5JTo92tt37Rv4CrLhKAvJSaVwmNsvRKWev3CDcPSc/Divider%202.png)\n\nIn Iot infrastructure, data is coming from multiple sides and resources in variant forms is usually unstructured. Here, in processing life cycle firstly data is organized in smooth machine readable form called input. \n\nIn second step, processing module converts data into structured and valuable form via multiple techniques and by performing different mathematical and logical operations on it called processing. Although processed data is available after processing phase, yet it is intended to be in human readable termed as output. Output is necessary to produce an insight view of all the functioning. \n\n<center>![](https://i.imgur.com/V2jDKZ3.png)</center>\nProcessing Life Cycle’s detailed view (Personally Edited on PowerPoint)\n\n![Divider 2.png](https://cdn.steemitimages.com/DQmcku5JTo92tt37Rv4CrLhKAvJSaVwmNsvRKWev3CDcPSc/Divider%202.png)\n\nMany cost effective and efficient tools are available for data processing in IoT infrastructures listed as:\n\n1.\t**Google cloud** is tremendously used for managing, organizing and exchanging data. It capable the businesses to see the real-time view of business. \n\n2.\t**Microsoft Azure** is more flexible to develop multiple solutions for variant nature businesses.\n\n3.\t**IBM Waston** assists the businesses by providing them a platform to perform various operation on data like communicating, collecting and sharing data from wearable gadgets, handhelds ad embedded devices. It’s a cost effective tool for increasing expertise in related domains.\n\n4.\t**Oracle IoT Suit** facilitate its users by providing a platform to extend the existing systems in real-time and manage them via change management process.\n\n5.\t**Amazon AWS** gives end-to-end connection, user interface and better data processing even when connected devices are offline.\n\n6.\t**Cisco Cloud** gives end-to-end connection, user interface and better data processing even when connected devices are offline.\n \n\n\nHence, all the above mentioned tools are cost effective, versatile and perform data processing in sustainable and reliable manner.\n\n![Divider 2.png](https://cdn.steemitimages.com/DQmcku5JTo92tt37Rv4CrLhKAvJSaVwmNsvRKWev3CDcPSc/Divider%202.png)\n\nI am also thankful to the management of SIZ for providing such a great platform where we can show our talent and skills.</div>\n\nCC: @cryptokraze @siz-official\n\n![Footer.png](https://cdn.steemitimages.com/DQmZC7bpcF5HuGVbgJu6KZDyRj3JrvcgCiay5mAsFSMyhU6/Footer.png)",
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      "body": "<div class=\"text-justify\">\n\n\nIn IoT infrastructures, “data ingestion is the process where data is prepared for analysis, by integrating and organizing various kinds of data coming from multiple resources into a single repository.” \n\nThis single repository can be a database, a processing system or may be a data warehouse. In data ingestion process data of variant nature coming from different resources is reconciled, organized and metadata is linked. This process gives control over how the data came, when the data came and where it is placed.\n\n![Figure_4  Data Ingestion and data management in IoT.png](https://cdn.steemitimages.com/DQmQqgyojZ8EueXrR2TwA6KKJir9dTLGdNy8iTUw4aQ4bHw/Figure_4%20%20Data%20Ingestion%20and%20data%20management%20in%20IoT.png)\n\n\nData ingestion works on the following principles:\n\n1.\tNo replication of data\n2.\tData must be understandable and reliable\n3.\tBroad support for objectives and targets\n4.\tMulti-structure data support\n5.\tExtensive ETL operations\n6.\tHybrid use and deployment\n7.\tFully foundational\n \n![Divider 2.png](https://cdn.steemitimages.com/DQmcku5JTo92tt37Rv4CrLhKAvJSaVwmNsvRKWev3CDcPSc/Divider%202.png)\n\nData ingestion is an intellectual and critical aspect of data management dealt by data science. It provides the facility of holistic insight view of data, thus, have great importance for business entities. Data ingestion depends on IoT architectures and models of the system. \n\nThere are two main frameworks used for data ingestion in IoT:\n1.\tBatch data ingestion\n2.\tStream data ingestion\n\n**Batch Data Ingestion**\nIn batch data ingestion, huge influx of data is divided into data batches periodically and these data batches are further organized. It requires few resources. Thus, it is cost effective framework.\n\n**Stream Data Ingestion**\nIn stream data ingestion, massive data is divided into streams continuously and these data streams are further processed. This framework is mostly used in IoT system designs. Somehow it’s costly as it needs more resources. Many tools are being used for data ingestion. Some of these tools are Apache Gobblin, Google Cloud data fusion, Equalum, Kafka, Azure 4.0 etc.\n\n\n![Figure_5 Data Management in IoT.jpg](https://cdn.steemitimages.com/DQmWpnT4Pyns7j4BUuixuegk5Se3kbXf2JqzRHAFzjYtinT/Figure_5%20Data%20Management%20in%20IoT.jpg)\n\n\n\n![Divider 2.png](https://cdn.steemitimages.com/DQmcku5JTo92tt37Rv4CrLhKAvJSaVwmNsvRKWev3CDcPSc/Divider%202.png)\n\n\n\n![Footer.png](https://cdn.steemitimages.com/DQmZC7bpcF5HuGVbgJu6KZDyRj3JrvcgCiay5mAsFSMyhU6/Footer.png)\n\n\nI am also thankful to the management of SIZ for providing such a great platform where we can show our talent and skills.</div>\n\nCC: @cryptokraze @siz-official",
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