[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123048-en":3,"doc-seo-123048-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123048,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Algorithms - Optimizing Efficiency in AI Applications","Machine learning (ML) is presented as an AI approach that builds programs and data models capable of performing tasks without direct instruction. The work outlines three learning paradigms: guided learning, uncontrolled learning, and reinforcement learning, and explains how they support real-time decision systems and autonomous technologies. It describes how tuning and techniques such as feature selection, dimensionality reduction, model editing, and compression improve performance and accuracy. Practical efficiency gains are linked to faster processing, lower costs, scalability, and greener deployment through model compression, transfer learning, and edge computing.","Machine Learning Algorithms: Optimizing Efficiency in AI Applications  \nBalkrishna Rasiklal Yadav*  \nIndependent Researcher, INDIA  \n*Corresponding Author: Balkrishna Rasiklal Yadav  \nReceived: 26-09-2024 Revised: 10-10-2024 Accepted: 28-10-2024  \nABSTRACT  \nMachine learning (ML) is an AI technology that creates programs and data models that can perform tasks without being instructed. It has three major types: guided learning, uncontrolled learning, and reinforcement learning. ML is essential for big projects like real-time decision-making systems and self-driving cars, robots, and drones. It improves AI systems by making it easier to create models, work with data, and run algorithms. ML algorithms have different types of learning, require different amounts of data and training times, and can be improved by tuning hyperparameters. Techniques like feature selection, dimensionality reduction, model editing, and compression can improve performance and accuracy in various fields. In the real world, making AI apps more efficient can lead to more options, lower prices, and faster processing. Key techniques like model compression, transfer learning, and edge computing are needed to achieve these goals.  \nKeywords--- Guided Learning, Uncontrolled Learning, Reinforcement Learning, Artificial Intelligence, Machine Learning  \nI. INTRODUCTION  \nBackground Information  \nMachine learning, or ML, is a branch of artificial intelligence that focuses on making programs and computer systems that can do things without being told to. It is built on learning from data, which lets models find trends, make predictions, and change how they make decisions based on what people say. ML has changed a lot in the last few decades. Its roots can be traced back to early AI study in the 1950s and the rise of computers. Three main types of learning are guided learning, uncontrolled learning, and reinforcement learning [1] . Unsupervised learning looks for patterns in data that hasn't been named, supervised learning predicts what will happen, and reinforcement learning learns how to make decisions by giving prizes or punishments. For big projects like realtime decision-making tools, ML needs to be very efficient.  \nThis drive for speed has an effect on the hardware that ML models use and leads to new developments in algorithmic design and optimization methods [2] . A key part of artificial intelligence (AI) is machine learning (ML), which lets systems learn and get better from experience without being explicitly programmed todo so. It makes current AI apps smarter, like voice recognition, picture analysis, decision-making, and systems that run themselves. ML improves the way decisions are made by letting AI systems guess what will happen and make the best choices in real time, without any help from a person.  \nDeep learning, computer vision, speech and audio processing, and natural language processing are all types of AI that use machine learning to learn how to perceive and handle sense data. In addition, it improves automation and autonomy by letting systems work on their own in robots, self-driving cars, and drones. ML is also a big part of making prediction analytics better. This type of analytics uses past data to guess what will happen in the future. In healthcare, ML models can predict how patients will do, when diseases will spread, or how to find patients who are at high risk. In marketing and retail, machine learning lets AI systems suggest goods based on what a user has looked at and bought in the past. This creates more personalized shopping experiences. Machine learning improves the performance of AI systems by making things like creating models, handling data, and making algorithms work more efficiently. As a result, AI programs use fewer resources while still running quickly. ML also makes it easier for humans and AI to work together by making smart ideas, helping people make decisions, and eliminating boring jobs. As machine learning models are s","cbCairF1ot9nrJuj","https://ap.wps.com/l/cbCairF1ot9nrJuj","pdf",379396,1,9,"English","en",105,"# Introduction\n## Background Information\n## Efficiency in AI Applications\n## Real-World Impact and Key Techniques","[{\"question\":\"How does efficient ML help deployment on edge devices?\",\"answer\":\"Efficient models reduce dependence on cloud computing by enabling inference closer to where data is generated. This lowers latency and transmission costs, making edge-based applications more practical.\"}]","Machine Learning Algorithms - Optimizing Efficiency in AI Applications | PDF",1785814382,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-algorithms-optimizing-efficiency-in-ai-applications","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-algorithms-optimizing-efficiency-in-ai-applications/123048/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How does efficient ML help deployment on edge devices?","Question",{"text":75,"@type":76},"Efficient models reduce dependence on cloud computing by enabling inference closer to where data is generated. 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