{"id":27848,"date":"2025-07-24T12:33:07","date_gmt":"2025-07-24T09:33:07","guid":{"rendered":"https:\/\/onkupon.com\/?post_type=courses&#038;p=27848"},"modified":"2025-07-24T09:33:08","modified_gmt":"2025-07-24T09:33:08","slug":"llm-and-text-processing","status":"publish","type":"courses","link":"https:\/\/onkupon.com\/en\/egitim\/llm-and-text-processing\/","title":{"rendered":"Large Language Models (LLM) and Text Processing"},"content":{"rendered":"<h3 data-start=\"178\" data-end=\"216\">Large Language Models (LLM) ve Text Processing<\/h3>\n<h3 data-start=\"178\" data-end=\"216\">1 | Purpose &amp; Industry Rationale<\/h3>\n<ul data-start=\"218\" data-end=\"808\">\n<li data-start=\"218\" data-end=\"417\">\n<p data-start=\"220\" data-end=\"417\"><strong data-start=\"220\" data-end=\"239\">Market Momentum<\/strong>\u2002The global Natural Language Processing (NLP) market, valued at <strong data-start=\"303\" data-end=\"331\">USD 25.9 billion in 2023<\/strong>, is projected to reach <strong data-start=\"355\" data-end=\"384\">USD 189.6 billion by 2030<\/strong>, reflecting a <strong data-start=\"399\" data-end=\"414\">32.5 % CAGR<\/strong>.<\/p>\n<\/li>\n<li data-start=\"418\" data-end=\"640\">\n<p data-start=\"420\" data-end=\"640\"><strong data-start=\"420\" data-end=\"436\">Talent Surge<\/strong>\u2002LinkedIn\u2019s <em data-start=\"448\" data-end=\"464\">Future of Work<\/em> report shows that AI\/ML job postings grew <strong data-start=\"507\" data-end=\"515\">38 %<\/strong> between 2020 and 2024, while positions requiring generative-AI and LLM skills expanded exponentially over the same period.<\/p>\n<\/li>\n<li data-start=\"641\" data-end=\"808\">\n<p data-start=\"643\" data-end=\"808\"><strong data-start=\"643\" data-end=\"662\">Salary Premiums<\/strong>\u2002As of 2025, U.S. <em>Large Language Models\u00a0Engineer<\/em> roles command average annual salaries of <strong data-start=\"736\" data-end=\"761\">USD 180 000 \u2013 240 000<\/strong>, with senior positions exceeding this range.<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"810\" data-end=\"1024\">Large Language Models (LLM) and Text Processing certification provides a quantitative, globally verifiable credential for professionals who can <strong data-start=\"911\" data-end=\"1023\">transform raw text into rich representations, design advanced LLM solutions, and deploy them into production<\/strong>.<\/p>\n<hr data-start=\"1026\" data-end=\"1029\" \/>\n<h3 data-start=\"1031\" data-end=\"1058\">2 | Large Language Models (LLM) and Text Processing Exam Architecture<\/h3>\n<div class=\"_tableContainer_80l1q_1\">\n<div class=\"_tableWrapper_80l1q_14 group flex w-fit flex-col-reverse\">\n<table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"1060\" data-end=\"1421\">\n<thead data-start=\"1060\" data-end=\"1081\">\n<tr data-start=\"1060\" data-end=\"1081\">\n<th data-start=\"1060\" data-end=\"1070\" data-col-size=\"sm\">Feature<\/th>\n<th data-start=\"1070\" data-end=\"1081\" data-col-size=\"md\">Details<\/th>\n<\/tr>\n<\/thead>\n<tbody data-start=\"1104\" data-end=\"1421\">\n<tr data-start=\"1104\" data-end=\"1160\">\n<td data-start=\"1104\" data-end=\"1120\" data-col-size=\"sm\"><strong data-start=\"1106\" data-end=\"1119\">Questions<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1120\" data-end=\"1160\">50 multiple-choice (single correct)<\/td>\n<\/tr>\n<tr data-start=\"1161\" data-end=\"1214\">\n<td data-start=\"1161\" data-end=\"1176\" data-col-size=\"sm\"><strong data-start=\"1163\" data-end=\"1175\">Duration<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1176\" data-end=\"1214\">120 minutes (\u2248 1 min per question)<\/td>\n<\/tr>\n<tr data-start=\"1215\" data-end=\"1239\">\n<td data-start=\"1215\" data-end=\"1231\" data-col-size=\"sm\"><strong data-start=\"1217\" data-end=\"1230\">Pass Mark<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1231\" data-end=\"1239\">70 %<\/td>\n<\/tr>\n<tr data-start=\"1240\" data-end=\"1302\">\n<td data-start=\"1240\" data-end=\"1255\" data-col-size=\"sm\"><strong data-start=\"1242\" data-end=\"1254\">Delivery<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1255\" data-end=\"1302\">Online, remotely proctored, instant scoring<\/td>\n<\/tr>\n<tr data-start=\"1303\" data-end=\"1368\">\n<td data-start=\"1303\" data-end=\"1320\" data-col-size=\"sm\"><strong data-start=\"1305\" data-end=\"1319\">Credential<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1320\" data-end=\"1368\">Web +\u00a0 QR verification<\/td>\n<\/tr>\n<tr data-start=\"1369\" data-end=\"1421\">\n<td data-start=\"1369\" data-end=\"1383\" data-col-size=\"sm\"><strong data-start=\"1371\" data-end=\"1382\">Retakes<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1383\" data-end=\"1421\">Two free attempts within 12 months<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"sticky end-(--thread-content-margin) h-0 self-end select-none\">\n<div class=\"absolute end-0 flex items-end\">\u00a0<\/div>\n<\/div>\n<\/div>\n<\/div>\n<hr data-start=\"1423\" data-end=\"1426\" \/>\n<h3 data-start=\"1428\" data-end=\"1465\">3 | Assessed Competency Domains<\/h3>\n<p data-start=\"1466\" data-end=\"1545\"><em data-start=\"1466\" data-end=\"1543\">(each row contains a representative stem drawn from the uploaded item bank)<\/em><\/p>\n<div class=\"_tableContainer_80l1q_1\">\n<div class=\"_tableWrapper_80l1q_14 group flex w-fit flex-col-reverse\">\n<table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"1547\" data-end=\"2624\">\n<thead data-start=\"1547\" data-end=\"1572\">\n<tr data-start=\"1547\" data-end=\"1572\">\n<th data-start=\"1547\" data-end=\"1556\" data-col-size=\"md\">Domain<\/th>\n<th data-start=\"1556\" data-end=\"1572\" data-col-size=\"md\">Example Stem<\/th>\n<\/tr>\n<\/thead>\n<tbody data-start=\"1599\" data-end=\"2624\">\n<tr data-start=\"1599\" data-end=\"1699\">\n<td data-start=\"1599\" data-end=\"1634\" data-col-size=\"md\"><strong data-start=\"1601\" data-end=\"1633\">Text Cleaning &amp; Tokenization<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1634\" data-end=\"1699\">\u201cWhat is the principal difference between BPE and WordPiece?\u201d<\/td>\n<\/tr>\n<tr data-start=\"1700\" data-end=\"1821\">\n<td data-start=\"1700\" data-end=\"1739\" data-col-size=\"md\"><strong data-start=\"1702\" data-end=\"1738\">Vectorization &amp; Embedding Spaces<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1739\" data-end=\"1821\">\u201cIn which scenarios is <em data-start=\"1764\" data-end=\"1783\">cosine similarity<\/em> preferred over Euclidean distance?\u201d<\/td>\n<\/tr>\n<tr data-start=\"1822\" data-end=\"1953\">\n<td data-start=\"1822\" data-end=\"1864\" data-col-size=\"md\"><strong data-start=\"1824\" data-end=\"1863\">Transformers &amp; Attention Mechanisms<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1864\" data-end=\"1953\">\u201cWhy is the <em data-start=\"1878\" data-end=\"1893\">d&lt;sub&gt;k&lt;\/sub&gt;<\/em> scaling factor required in Scaled-Dot-Product Attention?\u201d<\/td>\n<\/tr>\n<tr data-start=\"1954\" data-end=\"2062\">\n<td data-start=\"1954\" data-end=\"1992\" data-col-size=\"md\"><strong data-start=\"1956\" data-end=\"1991\">LLM Architecture &amp; Scaling Laws<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"1992\" data-end=\"2062\">\u201cWhat is the practical implication of the Chinchilla scaling law?\u201d<\/td>\n<\/tr>\n<tr data-start=\"2063\" data-end=\"2169\">\n<td data-start=\"2063\" data-end=\"2097\" data-col-size=\"md\"><strong data-start=\"2065\" data-end=\"2096\">Prompt Design &amp; Engineering<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"2097\" data-end=\"2169\">\u201cHow do zero-shot, few-shot, and chain-of-thought prompting differ?\u201d<\/td>\n<\/tr>\n<tr data-start=\"2170\" data-end=\"2300\">\n<td data-start=\"2170\" data-end=\"2218\" data-col-size=\"md\"><strong data-start=\"2172\" data-end=\"2217\">Fine-Tuning &amp; Adaptation (FT, LoRA, PEFT)<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"2218\" data-end=\"2300\">\u201cWhy are LoRA layers favoured over full back-propagation in certain settings?\u201d<\/td>\n<\/tr>\n<tr data-start=\"2301\" data-end=\"2420\">\n<td data-start=\"2301\" data-end=\"2330\" data-col-size=\"md\"><strong data-start=\"2303\" data-end=\"2329\">RAG &amp; Vector Databases<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"2330\" data-end=\"2420\">\u201cUnder what circumstances do dense and sparse retrievers achieve similar performance?\u201d<\/td>\n<\/tr>\n<tr data-start=\"2421\" data-end=\"2515\">\n<td data-start=\"2421\" data-end=\"2446\" data-col-size=\"md\"><strong data-start=\"2423\" data-end=\"2445\">Evaluation Metrics<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"2446\" data-end=\"2515\">\u201cList advantages and drawbacks of BERTScore relative to ROUGE-L.\u201d<\/td>\n<\/tr>\n<tr data-start=\"2516\" data-end=\"2624\">\n<td data-start=\"2516\" data-end=\"2553\" data-col-size=\"md\"><strong data-start=\"2518\" data-end=\"2552\">Ethics, Safety &amp; Hallucination<\/strong><\/td>\n<td data-col-size=\"md\" data-start=\"2553\" data-end=\"2624\">\u201cWhich vulnerability is uncovered by prompt-based jailbreak tests?\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"sticky end-(--thread-content-margin) h-0 self-end select-none\">\n<div class=\"absolute end-0 flex items-end\">\u00a0<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p data-start=\"2626\" data-end=\"2767\">Questions target the <em data-start=\"2647\" data-end=\"2662\">Apply\u2013Analyze<\/em> levels of Bloom\u2019s taxonomy; pilot testing produced <strong data-start=\"2714\" data-end=\"2737\">Cronbach\u2019s \u03b1 = 0.83<\/strong>, indicating high reliability.<\/p>\n<hr data-start=\"2769\" data-end=\"2772\" \/>\n<h3 data-start=\"2774\" data-end=\"2813\">4 | Certificate Value Proposition<\/h3>\n<div class=\"_tableContainer_80l1q_1\">\n<div class=\"_tableWrapper_80l1q_14 group flex w-fit flex-col-reverse\">\n<table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"2815\" data-end=\"3338\">\n<thead data-start=\"2815\" data-end=\"2840\">\n<tr data-start=\"2815\" data-end=\"2840\">\n<th data-start=\"2815\" data-end=\"2825\" data-col-size=\"sm\">Benefit<\/th>\n<th data-start=\"2825\" data-end=\"2840\" data-col-size=\"lg\">Explanation<\/th>\n<\/tr>\n<\/thead>\n<tbody data-start=\"2867\" data-end=\"3338\">\n<tr data-start=\"2867\" data-end=\"2956\">\n<td data-start=\"2867\" data-end=\"2892\" data-col-size=\"sm\"><strong data-start=\"2869\" data-end=\"2891\">Global Recognition<\/strong><\/td>\n<td data-col-size=\"lg\" data-start=\"2892\" data-end=\"2956\">The badge verifies on LinkedIn \/ GitHub with a single click.<\/td>\n<\/tr>\n<tr data-start=\"2957\" data-end=\"3094\">\n<td data-start=\"2957\" data-end=\"2993\" data-col-size=\"sm\"><strong data-start=\"2959\" data-end=\"2992\">Career Velocity &amp; Salary Lift<\/strong><\/td>\n<td data-col-size=\"lg\" data-start=\"2993\" data-end=\"3094\">Certified LLM specialists are hired faster and negotiate <strong data-start=\"3052\" data-end=\"3073\">top-band salaries<\/strong> relative to peers.<\/td>\n<\/tr>\n<tr data-start=\"3095\" data-end=\"3188\">\n<td data-start=\"3095\" data-end=\"3125\" data-col-size=\"sm\"><strong data-start=\"3097\" data-end=\"3124\">Bid &amp; Proposal Strength<\/strong><\/td>\n<td data-col-size=\"lg\" data-start=\"3125\" data-end=\"3188\">Documented expertise earns bonus points in RFP evaluations.<\/td>\n<\/tr>\n<tr data-start=\"3189\" data-end=\"3338\">\n<td data-start=\"3189\" data-end=\"3225\" data-col-size=\"sm\"><strong data-start=\"3191\" data-end=\"3224\">Continuous Learning Ecosystem<\/strong><\/td>\n<td data-col-size=\"lg\" data-start=\"3225\" data-end=\"3338\">Successful candidates receive a 25 % OptiWisdom Data Academy discount plus monthly live code-review sessions.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"sticky end-(--thread-content-margin) h-0 self-end select-none\">\n<div class=\"absolute end-0 flex items-end\">\u00a0<\/div>\n<\/div>\n<\/div>\n<\/div>\n<hr data-start=\"3340\" data-end=\"3343\" \/>\n<h3 data-start=\"3345\" data-end=\"3383\">5 | Scientific Design &amp; Validity<\/h3>\n<ul data-start=\"3385\" data-end=\"3681\">\n<li data-start=\"3385\" data-end=\"3461\">\n<p data-start=\"3387\" data-end=\"3461\"><strong data-start=\"3387\" data-end=\"3411\">Item Response Theory<\/strong> was applied to remove low-discrimination items.<\/p>\n<\/li>\n<li data-start=\"3462\" data-end=\"3544\">\n<p data-start=\"3464\" data-end=\"3544\"><strong data-start=\"3464\" data-end=\"3497\">Differential Item Functioning<\/strong> audits minimized cultural \/ linguistic bias.<\/p>\n<\/li>\n<li data-start=\"3545\" data-end=\"3681\">\n<p data-start=\"3547\" data-end=\"3681\"><strong data-start=\"3547\" data-end=\"3569\">Technical Currency<\/strong>\u2002All scenarios align with Python 3.12, Hugging Face Transformers 0.22, FAISS 1.8, and LangChain 0.2 (Q2 2025).<\/p>\n<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>Large Language Models (LLM) ve Text Processing 1 | Purpose &amp; Industry Rationale Market Momentum\u2002The global Natural Language Processing (NLP) market, valued at USD 25.9 billion in 2023, is projected to reach USD 189.6 billion by 2030, reflecting a 32.5 % CAGR. Talent Surge\u2002LinkedIn\u2019s Future of Work report shows that AI\/ML job postings grew 38 [&hellip;]<\/p>\n","protected":false},"author":18,"featured_media":27927,"template":"","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center 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