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joe-naz01/README.md

Hi there 👋

🎓 Data Science and Machine Learning Portfolio

Welcome to my collection of applied Data Science and Machine Learning projects built using Python, scikit-learn, PyTorch, and transformer-based models. Each project demonstrates end-to-end workflows — from data preprocessing and feature engineering to model development, tuning, and interpretation.

DATA SCIENCE AND MACHINE LEARNING

  1. Supervised learning with scikit learn

  2. Unsupervised learning in Python

  3. Linear Classifiers in Python

  4. Machine Learning for Time-Series Data in Python

  5. Machine Learning with Tree-Based Models in Python

  6. Extreme Gradient Boosting with XGBoost

LARGE LANGUAGE MODELS

  1. Basics of LLMs in Python

  2. Deep Learning for Text with Pytorch

  3. Transformer Models with PyTorch

  4. Llama Fundamentals

  5. Developing LLM Applications with LangChain

Popular repositories Loading

  1. DRO-Matic DRO-MaticPublic

    C++

  2. Plant_AI Plant_AIPublic

    Forked from soumyajit4419/Plant_AI

    Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

    Jupyter Notebook

  3. Image-Classification Image-ClassificationPublic

    Forked from CheshtaK/Image-Classification

    Image Classification using SVM

    Jupyter Notebook

  4. Plant-Leaf-Disease-Detection-using-SVM Plant-Leaf-Disease-Detection-using-SVMPublic

    Forked from manojkumar101/Plant-Leaf-Disease-Detection-using-SVM

    Single model which will be capable for detection of disease in various types of farming practices like floriculture, arboriculture, agriculture, cultivation, horticulture, etc.

    Jupyter Notebook

  5. html-portfolio html-portfolioPublic

    HTML

  6. image-classification-28x28 image-classification-28x28Public

    Develop and evaluate machine learning classifiers to categorize 28x28 grayscale images into predefined classes. Multiple algorithms are implemented and compared to identify the most accurate and co…

    Jupyter Notebook

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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joe-naz01/README.md

Hi there 👋

🎓 Data Science and Machine Learning Portfolio

Welcome to my collection of applied Data Science and Machine Learning projects built using Python, scikit-learn, PyTorch, and transformer-based models. Each project demonstrates end-to-end workflows — from data preprocessing and feature engineering to model development, tuning, and interpretation.

DATA SCIENCE AND MACHINE LEARNING

  1. Supervised learning with scikit learn

  2. Unsupervised learning in Python

  3. Linear Classifiers in Python

  4. Machine Learning for Time-Series Data in Python

  5. Machine Learning with Tree-Based Models in Python

  6. Extreme Gradient Boosting with XGBoost

LARGE LANGUAGE MODELS

  1. Basics of LLMs in Python

  2. Deep Learning for Text with Pytorch

  3. Transformer Models with PyTorch

  4. Llama Fundamentals

  5. Developing LLM Applications with LangChain

Popular repositories Loading

  1. DRO-Matic DRO-MaticPublic

    C++

  2. Plant_AI Plant_AIPublic

    Forked from soumyajit4419/Plant_AI

    Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

    Jupyter Notebook

  3. Image-Classification Image-ClassificationPublic

    Forked from CheshtaK/Image-Classification

    Image Classification using SVM

    Jupyter Notebook

  4. Plant-Leaf-Disease-Detection-using-SVM Plant-Leaf-Disease-Detection-using-SVMPublic

    Forked from manojkumar101/Plant-Leaf-Disease-Detection-using-SVM

    Single model which will be capable for detection of disease in various types of farming practices like floriculture, arboriculture, agriculture, cultivation, horticulture, etc.

    Jupyter Notebook

  5. html-portfolio html-portfolioPublic

    HTML

  6. image-classification-28x28 image-classification-28x28Public

    Develop and evaluate machine learning classifiers to categorize 28x28 grayscale images into predefined classes. Multiple algorithms are implemented and compared to identify the most accurate and co…

    Jupyter Notebook

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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joe-naz01/README.md

Hi there 👋

🎓 Data Science and Machine Learning Portfolio

Welcome to my collection of applied Data Science and Machine Learning projects built using Python, scikit-learn, PyTorch, and transformer-based models. Each project demonstrates end-to-end workflows — from data preprocessing and feature engineering to model development, tuning, and interpretation.

DATA SCIENCE AND MACHINE LEARNING

  1. Supervised learning with scikit learn

  2. Unsupervised learning in Python

  3. Linear Classifiers in Python

  4. Machine Learning for Time-Series Data in Python

  5. Machine Learning with Tree-Based Models in Python

  6. Extreme Gradient Boosting with XGBoost

LARGE LANGUAGE MODELS

  1. Basics of LLMs in Python

  2. Deep Learning for Text with Pytorch

  3. Transformer Models with PyTorch

  4. Llama Fundamentals

  5. Developing LLM Applications with LangChain

Popular repositories Loading

  1. DRO-Matic DRO-MaticPublic

    C++

  2. Plant_AI Plant_AIPublic

    Forked from soumyajit4419/Plant_AI

    Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

    Jupyter Notebook

  3. Image-Classification Image-ClassificationPublic

    Forked from CheshtaK/Image-Classification

    Image Classification using SVM

    Jupyter Notebook

  4. Plant-Leaf-Disease-Detection-using-SVM Plant-Leaf-Disease-Detection-using-SVMPublic

    Forked from manojkumar101/Plant-Leaf-Disease-Detection-using-SVM

    Single model which will be capable for detection of disease in various types of farming practices like floriculture, arboriculture, agriculture, cultivation, horticulture, etc.

    Jupyter Notebook

  5. html-portfolio html-portfolioPublic

    HTML

  6. image-classification-28x28 image-classification-28x28Public

    Develop and evaluate machine learning classifiers to categorize 28x28 grayscale images into predefined classes. Multiple algorithms are implemented and compared to identify the most accurate and co…

    Jupyter Notebook

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
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joe-naz01/README.md

Hi there 👋

🎓 Data Science and Machine Learning Portfolio

Welcome to my collection of applied Data Science and Machine Learning projects built using Python, scikit-learn, PyTorch, and transformer-based models. Each project demonstrates end-to-end workflows — from data preprocessing and feature engineering to model development, tuning, and interpretation.

DATA SCIENCE AND MACHINE LEARNING

  1. Supervised learning with scikit learn

  2. Unsupervised learning in Python

  3. Linear Classifiers in Python

  4. Machine Learning for Time-Series Data in Python

  5. Machine Learning with Tree-Based Models in Python

  6. Extreme Gradient Boosting with XGBoost

LARGE LANGUAGE MODELS

  1. Basics of LLMs in Python

  2. Deep Learning for Text with Pytorch

  3. Transformer Models with PyTorch

  4. Llama Fundamentals

  5. Developing LLM Applications with LangChain

Popular repositories Loading

  1. DRO-Matic DRO-MaticPublic

    C++

  2. Plant_AI Plant_AIPublic

    Forked from soumyajit4419/Plant_AI

    Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

    Jupyter Notebook

  3. Image-Classification Image-ClassificationPublic

    Forked from CheshtaK/Image-Classification

    Image Classification using SVM

    Jupyter Notebook

  4. Plant-Leaf-Disease-Detection-using-SVM Plant-Leaf-Disease-Detection-using-SVMPublic

    Forked from manojkumar101/Plant-Leaf-Disease-Detection-using-SVM

    Single model which will be capable for detection of disease in various types of farming practices like floriculture, arboriculture, agriculture, cultivation, horticulture, etc.

    Jupyter Notebook

  5. html-portfolio html-portfolioPublic

    HTML

  6. image-classification-28x28 image-classification-28x28Public

    Develop and evaluate machine learning classifiers to categorize 28x28 grayscale images into predefined classes. Multiple algorithms are implemented and compared to identify the most accurate and co…

    Jupyter Notebook

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

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Report abuse
joe-naz01/README.md

Hi there 👋

🎓 Data Science and Machine Learning Portfolio

Welcome to my collection of applied Data Science and Machine Learning projects built using Python, scikit-learn, PyTorch, and transformer-based models. Each project demonstrates end-to-end workflows — from data preprocessing and feature engineering to model development, tuning, and interpretation.

DATA SCIENCE AND MACHINE LEARNING

  1. Supervised learning with scikit learn

  2. Unsupervised learning in Python

  3. Linear Classifiers in Python

  4. Machine Learning for Time-Series Data in Python

  5. Machine Learning with Tree-Based Models in Python

  6. Extreme Gradient Boosting with XGBoost

LARGE LANGUAGE MODELS

  1. Basics of LLMs in Python

  2. Deep Learning for Text with Pytorch

  3. Transformer Models with PyTorch

  4. Llama Fundamentals

  5. Developing LLM Applications with LangChain

Popular repositories Loading

  1. DRO-Matic DRO-MaticPublic

    C++

  2. Plant_AI Plant_AIPublic

    Forked from soumyajit4419/Plant_AI

    Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

    Jupyter Notebook

  3. Image-Classification Image-ClassificationPublic

    Forked from CheshtaK/Image-Classification

    Image Classification using SVM

    Jupyter Notebook

  4. Plant-Leaf-Disease-Detection-using-SVM Plant-Leaf-Disease-Detection-using-SVMPublic

    Forked from manojkumar101/Plant-Leaf-Disease-Detection-using-SVM

    Single model which will be capable for detection of disease in various types of farming practices like floriculture, arboriculture, agriculture, cultivation, horticulture, etc.

    Jupyter Notebook

  5. html-portfolio html-portfolioPublic

    HTML

  6. image-classification-28x28 image-classification-28x28Public

    Develop and evaluate machine learning classifiers to categorize 28x28 grayscale images into predefined classes. Multiple algorithms are implemented and compared to identify the most accurate and co…

    Jupyter Notebook

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
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joe-naz01/README.md

Hi there 👋

🎓 Data Science and Machine Learning Portfolio

Welcome to my collection of applied Data Science and Machine Learning projects built using Python, scikit-learn, PyTorch, and transformer-based models. Each project demonstrates end-to-end workflows — from data preprocessing and feature engineering to model development, tuning, and interpretation.

DATA SCIENCE AND MACHINE LEARNING

  1. Supervised learning with scikit learn

  2. Unsupervised learning in Python

  3. Linear Classifiers in Python

  4. Machine Learning for Time-Series Data in Python

  5. Machine Learning with Tree-Based Models in Python

  6. Extreme Gradient Boosting with XGBoost

LARGE LANGUAGE MODELS

  1. Basics of LLMs in Python

  2. Deep Learning for Text with Pytorch

  3. Transformer Models with PyTorch

  4. Llama Fundamentals

  5. Developing LLM Applications with LangChain

Popular repositories Loading

  1. DRO-Matic DRO-MaticPublic

    C++

  2. Plant_AI Plant_AIPublic

    Forked from soumyajit4419/Plant_AI

    Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

    Jupyter Notebook

  3. Image-Classification Image-ClassificationPublic

    Forked from CheshtaK/Image-Classification

    Image Classification using SVM

    Jupyter Notebook

  4. Plant-Leaf-Disease-Detection-using-SVM Plant-Leaf-Disease-Detection-using-SVMPublic

    Forked from manojkumar101/Plant-Leaf-Disease-Detection-using-SVM

    Single model which will be capable for detection of disease in various types of farming practices like floriculture, arboriculture, agriculture, cultivation, horticulture, etc.

    Jupyter Notebook

  5. html-portfolio html-portfolioPublic

    HTML

  6. image-classification-28x28 image-classification-28x28Public

    Develop and evaluate machine learning classifiers to categorize 28x28 grayscale images into predefined classes. Multiple algorithms are implemented and compared to identify the most accurate and co…

    Jupyter Notebook

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
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Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
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Report abuse
joe-naz01/README.md

Hi there 👋

🎓 Data Science and Machine Learning Portfolio

Welcome to my collection of applied Data Science and Machine Learning projects built using Python, scikit-learn, PyTorch, and transformer-based models. Each project demonstrates end-to-end workflows — from data preprocessing and feature engineering to model development, tuning, and interpretation.

DATA SCIENCE AND MACHINE LEARNING

  1. Supervised learning with scikit learn

  2. Unsupervised learning in Python

  3. Linear Classifiers in Python

  4. Machine Learning for Time-Series Data in Python

  5. Machine Learning with Tree-Based Models in Python

  6. Extreme Gradient Boosting with XGBoost

LARGE LANGUAGE MODELS

  1. Basics of LLMs in Python

  2. Deep Learning for Text with Pytorch

  3. Transformer Models with PyTorch

  4. Llama Fundamentals

  5. Developing LLM Applications with LangChain

Popular repositories Loading

  1. DRO-Matic DRO-MaticPublic

    C++

  2. Plant_AI Plant_AIPublic

    Forked from soumyajit4419/Plant_AI

    Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

    Jupyter Notebook

  3. Image-Classification Image-ClassificationPublic

    Forked from CheshtaK/Image-Classification

    Image Classification using SVM

    Jupyter Notebook

  4. Plant-Leaf-Disease-Detection-using-SVM Plant-Leaf-Disease-Detection-using-SVMPublic

    Forked from manojkumar101/Plant-Leaf-Disease-Detection-using-SVM

    Single model which will be capable for detection of disease in various types of farming practices like floriculture, arboriculture, agriculture, cultivation, horticulture, etc.

    Jupyter Notebook

  5. html-portfolio html-portfolioPublic

    HTML

  6. image-classification-28x28 image-classification-28x28Public

    Develop and evaluate machine learning classifiers to categorize 28x28 grayscale images into predefined classes. Multiple algorithms are implemented and compared to identify the most accurate and co…

    Jupyter Notebook

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joe-naz01/README.md

Hi there 👋

🎓 Data Science and Machine Learning Portfolio

Welcome to my collection of applied Data Science and Machine Learning projects built using Python, scikit-learn, PyTorch, and transformer-based models. Each project demonstrates end-to-end workflows — from data preprocessing and feature engineering to model development, tuning, and interpretation.

DATA SCIENCE AND MACHINE LEARNING

  1. Supervised learning with scikit learn

  2. Unsupervised learning in Python

  3. Linear Classifiers in Python

  4. Machine Learning for Time-Series Data in Python

  5. Machine Learning with Tree-Based Models in Python

  6. Extreme Gradient Boosting with XGBoost

LARGE LANGUAGE MODELS

  1. Basics of LLMs in Python

  2. Deep Learning for Text with Pytorch

  3. Transformer Models with PyTorch

  4. Llama Fundamentals

  5. Developing LLM Applications with LangChain

Popular repositories Loading

  1. DRO-Matic DRO-MaticPublic

    C++

  2. Plant_AI Plant_AIPublic

    Forked from soumyajit4419/Plant_AI

    Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

    Jupyter Notebook

  3. Image-Classification Image-ClassificationPublic

    Forked from CheshtaK/Image-Classification

    Image Classification using SVM

    Jupyter Notebook

  4. Plant-Leaf-Disease-Detection-using-SVM Plant-Leaf-Disease-Detection-using-SVMPublic

    Forked from manojkumar101/Plant-Leaf-Disease-Detection-using-SVM

    Single model which will be capable for detection of disease in various types of farming practices like floriculture, arboriculture, agriculture, cultivation, horticulture, etc.

    Jupyter Notebook

  5. html-portfolio html-portfolioPublic

    HTML

  6. image-classification-28x28 image-classification-28x28Public

    Develop and evaluate machine learning classifiers to categorize 28x28 grayscale images into predefined classes. Multiple algorithms are implemented and compared to identify the most accurate and co…

    Jupyter Notebook