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AI Workshop Code Repository

Welcome to the AI Workshop Code Repository!
This repository is designed as a beginner-friendly, comprehensive guide to Artificial Intelligence (AI) and Machine Learning (ML) concepts, perfect for newcomers or those looking to deepen their understanding. Whether you’re attending our workshops or self-studying, this collection provides you with everything you need to get started with AI and ML.

Repository Structure

This repository is organized into various folders, each dedicated to a specific topic or algorithm, designed to offer a structured, step-by-step learning path. Each folder includes:

  • Jupyter Notebooks: Practical implementations of algorithms with explanations, covering a range of beginner to intermediate concepts in AI and ML.
  • Assignments: Carefully designed assignments to reinforce your understanding of each topic.
  • Solutions: Solutions to assignments to help guide you through the learning process.
  • Resources: Each folder includes a dedicated README file with curated links to supplementary articles, videos, and tutorials that will deepen your understanding of each topic.

Getting Started

1. Setting Up Your Environment

To get started with the code, you’ll need a Python environment with essential libraries like NumPy, Pandas, scikit-learn, and Matplotlib. We recommend using Anaconda or Jupyter Notebook for a smooth experience.

2. Topics Covered

This repository covers a variety of AI/ML topics, such as:

  • Data Preprocessing: Learn to clean and prepare data for ML models.
  • Model Tuning: Learn to optimize hyperparameters to improve a machine learning model's performance.
  • Supervised Learning: Implementation of algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
  • Unsupervised Learning: Explore Clustering techniques like K-Means, Hierarchical Clustering, and Dimensionality Reduction.
  • Neural Networks and Deep Learning: Introduction to concepts like Perceptrons, Feedforward Networks, and Convolutional Neural Networks.
  • Deep Computer Vision: Introduction to Object Detection, Image Segmentation and Image Classification.
  • Natural Language Processing (NLP): Basics of text processing, feature extraction, and text classification.
  • Model Evaluation: Techniques for evaluating model performance, such as confusion matrices, accuracy, and precision-recall.

3. Learning Path

Each folder is structured to build on the previous one. We recommend going through the folders in sequence for a smooth learning experience.

Resources and Additional Reading

Each folder contains a README file with carefully selected resources, including:

  • Articles: Introductory and advanced articles to understand the theoretical background.
  • Videos: Visual aids to reinforce the concepts.
  • Notebooks: Links to other example notebooks and code snippets to enhance your understanding.

Contributions

We encourage contributions! If you have ideas for additional resources, improvements to existing notebooks, or new algorithms to add, feel free to open a pull request or submit an issue. All contributions are welcome to make this repository more valuable for everyone.

Support

For any questions, feel free to reach out through the issue tracker or directly through our workshop's communication channels. We’re here to help you on your journey to mastering AI and ML!

Happy learning and coding!

About

This repository will contain all the codes discussed and displayed in the AI workshops

Resources

Readme
Activity
Custom properties

Stars

16 stars

Watchers

0 watching

Forks

13 forks
Report repository

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, '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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AI Workshop Code Repository
Welcome to the AI Workshop Code Repository!

This repository is designed as a beginner-friendly, comprehensive guide to Artificial Intelligence (AI) and Machine Learning (ML) concepts, perfect for newcomers or those looking to deepen their understanding. Whether you’re attending our workshops or self-studying, this collection provides you with everything you need to get started with AI and ML.
Repository Structure
This repository is organized into various folders, each dedicated to a specific topic or algorithm, designed to offer a structured, step-by-step learning path. Each folder includes:
  • Jupyter Notebooks: Practical implementations of algorithms with explanations, covering a range of beginner to intermediate concepts in AI and ML.
  • Assignments: Carefully designed assignments to reinforce your understanding of each topic.
  • Solutions: Solutions to assignments to help guide you through the learning process.
  • Resources: Each folder includes a dedicated README file with curated links to supplementary articles, videos, and tutorials that will deepen your understanding of each topic.
Getting Started
1. Setting Up Your Environment
To get started with the code, you’ll need a Python environment with essential libraries like NumPy, Pandas, scikit-learn, and Matplotlib. We recommend using Anaconda or Jupyter Notebook for a smooth experience.
2. Topics Covered
This repository covers a variety of AI/ML topics, such as:
  • Data Preprocessing: Learn to clean and prepare data for ML models.
  • Model Tuning: Learn to optimize hyperparameters to improve a machine learning model's performance.
  • Supervised Learning: Implementation of algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
  • Unsupervised Learning: Explore Clustering techniques like K-Means, Hierarchical Clustering, and Dimensionality Reduction.
  • Neural Networks and Deep Learning: Introduction to concepts like Perceptrons, Feedforward Networks, and Convolutional Neural Networks.
  • Deep Computer Vision: Introduction to Object Detection, Image Segmentation and Image Classification.
  • Natural Language Processing (NLP): Basics of text processing, feature extraction, and text classification.
  • Model Evaluation: Techniques for evaluating model performance, such as confusion matrices, accuracy, and precision-recall.
3. Learning Path
Each folder is structured to build on the previous one. We recommend going through the folders in sequence for a smooth learning experience.
Resources and Additional Reading
Each folder contains a README file with carefully selected resources, including:
  • Articles: Introductory and advanced articles to understand the theoretical background.
  • Videos: Visual aids to reinforce the concepts.
  • Notebooks: Links to other example notebooks and code snippets to enhance your understanding.
Contributions
We encourage contributions! If you have ideas for additional resources, improvements to existing notebooks, or new algorithms to add, feel free to open a pull request or submit an issue. All contributions are welcome to make this repository more valuable for everyone.
Support
For any questions, feel free to reach out through the issue tracker or directly through our workshop's communication channels. We’re here to help you on your journey to mastering AI and ML!
Happy learning and coding!
About
This repository will contain all the codes discussed and displayed in the AI workshops
Resources
Readme
Activity
Custom properties
Stars
16 stars
Watchers
0 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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AI Workshop Code Repository
Welcome to the AI Workshop Code Repository!

This repository is designed as a beginner-friendly, comprehensive guide to Artificial Intelligence (AI) and Machine Learning (ML) concepts, perfect for newcomers or those looking to deepen their understanding. Whether you’re attending our workshops or self-studying, this collection provides you with everything you need to get started with AI and ML.
Repository Structure
This repository is organized into various folders, each dedicated to a specific topic or algorithm, designed to offer a structured, step-by-step learning path. Each folder includes:
  • Jupyter Notebooks: Practical implementations of algorithms with explanations, covering a range of beginner to intermediate concepts in AI and ML.
  • Assignments: Carefully designed assignments to reinforce your understanding of each topic.
  • Solutions: Solutions to assignments to help guide you through the learning process.
  • Resources: Each folder includes a dedicated README file with curated links to supplementary articles, videos, and tutorials that will deepen your understanding of each topic.
Getting Started
1. Setting Up Your Environment
To get started with the code, you’ll need a Python environment with essential libraries like NumPy, Pandas, scikit-learn, and Matplotlib. We recommend using Anaconda or Jupyter Notebook for a smooth experience.
2. Topics Covered
This repository covers a variety of AI/ML topics, such as:
  • Data Preprocessing: Learn to clean and prepare data for ML models.
  • Model Tuning: Learn to optimize hyperparameters to improve a machine learning model's performance.
  • Supervised Learning: Implementation of algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
  • Unsupervised Learning: Explore Clustering techniques like K-Means, Hierarchical Clustering, and Dimensionality Reduction.
  • Neural Networks and Deep Learning: Introduction to concepts like Perceptrons, Feedforward Networks, and Convolutional Neural Networks.
  • Deep Computer Vision: Introduction to Object Detection, Image Segmentation and Image Classification.
  • Natural Language Processing (NLP): Basics of text processing, feature extraction, and text classification.
  • Model Evaluation: Techniques for evaluating model performance, such as confusion matrices, accuracy, and precision-recall.
3. Learning Path
Each folder is structured to build on the previous one. We recommend going through the folders in sequence for a smooth learning experience.
Resources and Additional Reading
Each folder contains a README file with carefully selected resources, including:
  • Articles: Introductory and advanced articles to understand the theoretical background.
  • Videos: Visual aids to reinforce the concepts.
  • Notebooks: Links to other example notebooks and code snippets to enhance your understanding.
Contributions
We encourage contributions! If you have ideas for additional resources, improvements to existing notebooks, or new algorithms to add, feel free to open a pull request or submit an issue. All contributions are welcome to make this repository more valuable for everyone.
Support
For any questions, feel free to reach out through the issue tracker or directly through our workshop's communication channels. We’re here to help you on your journey to mastering AI and ML!
Happy learning and coding!
About
This repository will contain all the codes discussed and displayed in the AI workshops
Resources
Readme
Activity
Custom properties
Stars
16 stars
Watchers
0 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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AI Workshop Code Repository
Welcome to the AI Workshop Code Repository!

This repository is designed as a beginner-friendly, comprehensive guide to Artificial Intelligence (AI) and Machine Learning (ML) concepts, perfect for newcomers or those looking to deepen their understanding. Whether you’re attending our workshops or self-studying, this collection provides you with everything you need to get started with AI and ML.
Repository Structure
This repository is organized into various folders, each dedicated to a specific topic or algorithm, designed to offer a structured, step-by-step learning path. Each folder includes:
  • Jupyter Notebooks: Practical implementations of algorithms with explanations, covering a range of beginner to intermediate concepts in AI and ML.
  • Assignments: Carefully designed assignments to reinforce your understanding of each topic.
  • Solutions: Solutions to assignments to help guide you through the learning process.
  • Resources: Each folder includes a dedicated README file with curated links to supplementary articles, videos, and tutorials that will deepen your understanding of each topic.
Getting Started
1. Setting Up Your Environment
To get started with the code, you’ll need a Python environment with essential libraries like NumPy, Pandas, scikit-learn, and Matplotlib. We recommend using Anaconda or Jupyter Notebook for a smooth experience.
2. Topics Covered
This repository covers a variety of AI/ML topics, such as:
  • Data Preprocessing: Learn to clean and prepare data for ML models.
  • Model Tuning: Learn to optimize hyperparameters to improve a machine learning model's performance.
  • Supervised Learning: Implementation of algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
  • Unsupervised Learning: Explore Clustering techniques like K-Means, Hierarchical Clustering, and Dimensionality Reduction.
  • Neural Networks and Deep Learning: Introduction to concepts like Perceptrons, Feedforward Networks, and Convolutional Neural Networks.
  • Deep Computer Vision: Introduction to Object Detection, Image Segmentation and Image Classification.
  • Natural Language Processing (NLP): Basics of text processing, feature extraction, and text classification.
  • Model Evaluation: Techniques for evaluating model performance, such as confusion matrices, accuracy, and precision-recall.
3. Learning Path
Each folder is structured to build on the previous one. We recommend going through the folders in sequence for a smooth learning experience.
Resources and Additional Reading
Each folder contains a README file with carefully selected resources, including:
  • Articles: Introductory and advanced articles to understand the theoretical background.
  • Videos: Visual aids to reinforce the concepts.
  • Notebooks: Links to other example notebooks and code snippets to enhance your understanding.
Contributions
We encourage contributions! If you have ideas for additional resources, improvements to existing notebooks, or new algorithms to add, feel free to open a pull request or submit an issue. All contributions are welcome to make this repository more valuable for everyone.
Support
For any questions, feel free to reach out through the issue tracker or directly through our workshop's communication channels. We’re here to help you on your journey to mastering AI and ML!
Happy learning and coding!
About
This repository will contain all the codes discussed and displayed in the AI workshops
Resources
Readme
Activity
Custom properties
Stars
16 stars
Watchers
0 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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" + '

Skip to content
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AI Workshop Code Repository
Welcome to the AI Workshop Code Repository!

This repository is designed as a beginner-friendly, comprehensive guide to Artificial Intelligence (AI) and Machine Learning (ML) concepts, perfect for newcomers or those looking to deepen their understanding. Whether you’re attending our workshops or self-studying, this collection provides you with everything you need to get started with AI and ML.
Repository Structure
This repository is organized into various folders, each dedicated to a specific topic or algorithm, designed to offer a structured, step-by-step learning path. Each folder includes:
  • Jupyter Notebooks: Practical implementations of algorithms with explanations, covering a range of beginner to intermediate concepts in AI and ML.
  • Assignments: Carefully designed assignments to reinforce your understanding of each topic.
  • Solutions: Solutions to assignments to help guide you through the learning process.
  • Resources: Each folder includes a dedicated README file with curated links to supplementary articles, videos, and tutorials that will deepen your understanding of each topic.
Getting Started
1. Setting Up Your Environment
To get started with the code, you’ll need a Python environment with essential libraries like NumPy, Pandas, scikit-learn, and Matplotlib. We recommend using Anaconda or Jupyter Notebook for a smooth experience.
2. Topics Covered
This repository covers a variety of AI/ML topics, such as:
  • Data Preprocessing: Learn to clean and prepare data for ML models.
  • Model Tuning: Learn to optimize hyperparameters to improve a machine learning model's performance.
  • Supervised Learning: Implementation of algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
  • Unsupervised Learning: Explore Clustering techniques like K-Means, Hierarchical Clustering, and Dimensionality Reduction.
  • Neural Networks and Deep Learning: Introduction to concepts like Perceptrons, Feedforward Networks, and Convolutional Neural Networks.
  • Deep Computer Vision: Introduction to Object Detection, Image Segmentation and Image Classification.
  • Natural Language Processing (NLP): Basics of text processing, feature extraction, and text classification.
  • Model Evaluation: Techniques for evaluating model performance, such as confusion matrices, accuracy, and precision-recall.
3. Learning Path
Each folder is structured to build on the previous one. We recommend going through the folders in sequence for a smooth learning experience.
Resources and Additional Reading
Each folder contains a README file with carefully selected resources, including:
  • Articles: Introductory and advanced articles to understand the theoretical background.
  • Videos: Visual aids to reinforce the concepts.
  • Notebooks: Links to other example notebooks and code snippets to enhance your understanding.
Contributions
We encourage contributions! If you have ideas for additional resources, improvements to existing notebooks, or new algorithms to add, feel free to open a pull request or submit an issue. All contributions are welcome to make this repository more valuable for everyone.
Support
For any questions, feel free to reach out through the issue tracker or directly through our workshop's communication channels. We’re here to help you on your journey to mastering AI and ML!
Happy learning and coding!
About
This repository will contain all the codes discussed and displayed in the AI workshops
Resources
Readme
Activity
Custom properties
Stars
16 stars
Watchers
0 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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AI Workshop Code Repository
Welcome to the AI Workshop Code Repository!

This repository is designed as a beginner-friendly, comprehensive guide to Artificial Intelligence (AI) and Machine Learning (ML) concepts, perfect for newcomers or those looking to deepen their understanding. Whether you’re attending our workshops or self-studying, this collection provides you with everything you need to get started with AI and ML.
Repository Structure
This repository is organized into various folders, each dedicated to a specific topic or algorithm, designed to offer a structured, step-by-step learning path. Each folder includes:
  • Jupyter Notebooks: Practical implementations of algorithms with explanations, covering a range of beginner to intermediate concepts in AI and ML.
  • Assignments: Carefully designed assignments to reinforce your understanding of each topic.
  • Solutions: Solutions to assignments to help guide you through the learning process.
  • Resources: Each folder includes a dedicated README file with curated links to supplementary articles, videos, and tutorials that will deepen your understanding of each topic.
Getting Started
1. Setting Up Your Environment
To get started with the code, you’ll need a Python environment with essential libraries like NumPy, Pandas, scikit-learn, and Matplotlib. We recommend using Anaconda or Jupyter Notebook for a smooth experience.
2. Topics Covered
This repository covers a variety of AI/ML topics, such as:
  • Data Preprocessing: Learn to clean and prepare data for ML models.
  • Model Tuning: Learn to optimize hyperparameters to improve a machine learning model's performance.
  • Supervised Learning: Implementation of algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
  • Unsupervised Learning: Explore Clustering techniques like K-Means, Hierarchical Clustering, and Dimensionality Reduction.
  • Neural Networks and Deep Learning: Introduction to concepts like Perceptrons, Feedforward Networks, and Convolutional Neural Networks.
  • Deep Computer Vision: Introduction to Object Detection, Image Segmentation and Image Classification.
  • Natural Language Processing (NLP): Basics of text processing, feature extraction, and text classification.
  • Model Evaluation: Techniques for evaluating model performance, such as confusion matrices, accuracy, and precision-recall.
3. Learning Path
Each folder is structured to build on the previous one. We recommend going through the folders in sequence for a smooth learning experience.
Resources and Additional Reading
Each folder contains a README file with carefully selected resources, including:
  • Articles: Introductory and advanced articles to understand the theoretical background.
  • Videos: Visual aids to reinforce the concepts.
  • Notebooks: Links to other example notebooks and code snippets to enhance your understanding.
Contributions
We encourage contributions! If you have ideas for additional resources, improvements to existing notebooks, or new algorithms to add, feel free to open a pull request or submit an issue. All contributions are welcome to make this repository more valuable for everyone.
Support
For any questions, feel free to reach out through the issue tracker or directly through our workshop's communication channels. We’re here to help you on your journey to mastering AI and ML!
Happy learning and coding!
About
This repository will contain all the codes discussed and displayed in the AI workshops
Resources
Readme
Activity
Custom properties
Stars
16 stars
Watchers
0 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
BranchesTags
Repository files navigation
AI Workshop Code Repository
Welcome to the AI Workshop Code Repository!

This repository is designed as a beginner-friendly, comprehensive guide to Artificial Intelligence (AI) and Machine Learning (ML) concepts, perfect for newcomers or those looking to deepen their understanding. Whether you’re attending our workshops or self-studying, this collection provides you with everything you need to get started with AI and ML.
Repository Structure
This repository is organized into various folders, each dedicated to a specific topic or algorithm, designed to offer a structured, step-by-step learning path. Each folder includes:
  • Jupyter Notebooks: Practical implementations of algorithms with explanations, covering a range of beginner to intermediate concepts in AI and ML.
  • Assignments: Carefully designed assignments to reinforce your understanding of each topic.
  • Solutions: Solutions to assignments to help guide you through the learning process.
  • Resources: Each folder includes a dedicated README file with curated links to supplementary articles, videos, and tutorials that will deepen your understanding of each topic.
Getting Started
1. Setting Up Your Environment
To get started with the code, you’ll need a Python environment with essential libraries like NumPy, Pandas, scikit-learn, and Matplotlib. We recommend using Anaconda or Jupyter Notebook for a smooth experience.
2. Topics Covered
This repository covers a variety of AI/ML topics, such as:
  • Data Preprocessing: Learn to clean and prepare data for ML models.
  • Model Tuning: Learn to optimize hyperparameters to improve a machine learning model's performance.
  • Supervised Learning: Implementation of algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
  • Unsupervised Learning: Explore Clustering techniques like K-Means, Hierarchical Clustering, and Dimensionality Reduction.
  • Neural Networks and Deep Learning: Introduction to concepts like Perceptrons, Feedforward Networks, and Convolutional Neural Networks.
  • Deep Computer Vision: Introduction to Object Detection, Image Segmentation and Image Classification.
  • Natural Language Processing (NLP): Basics of text processing, feature extraction, and text classification.
  • Model Evaluation: Techniques for evaluating model performance, such as confusion matrices, accuracy, and precision-recall.
3. Learning Path
Each folder is structured to build on the previous one. We recommend going through the folders in sequence for a smooth learning experience.
Resources and Additional Reading
Each folder contains a README file with carefully selected resources, including:
  • Articles: Introductory and advanced articles to understand the theoretical background.
  • Videos: Visual aids to reinforce the concepts.
  • Notebooks: Links to other example notebooks and code snippets to enhance your understanding.
Contributions
We encourage contributions! If you have ideas for additional resources, improvements to existing notebooks, or new algorithms to add, feel free to open a pull request or submit an issue. All contributions are welcome to make this repository more valuable for everyone.
Support
For any questions, feel free to reach out through the issue tracker or directly through our workshop's communication channels. We’re here to help you on your journey to mastering AI and ML!
Happy learning and coding!
About
This repository will contain all the codes discussed and displayed in the AI workshops
Resources
Readme
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16 stars
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0 watching
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13 forks
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AI Workshop Code Repository
Welcome to the AI Workshop Code Repository!

This repository is designed as a beginner-friendly, comprehensive guide to Artificial Intelligence (AI) and Machine Learning (ML) concepts, perfect for newcomers or those looking to deepen their understanding. Whether you’re attending our workshops or self-studying, this collection provides you with everything you need to get started with AI and ML.
Repository Structure
This repository is organized into various folders, each dedicated to a specific topic or algorithm, designed to offer a structured, step-by-step learning path. Each folder includes:
  • Jupyter Notebooks: Practical implementations of algorithms with explanations, covering a range of beginner to intermediate concepts in AI and ML.
  • Assignments: Carefully designed assignments to reinforce your understanding of each topic.
  • Solutions: Solutions to assignments to help guide you through the learning process.
  • Resources: Each folder includes a dedicated README file with curated links to supplementary articles, videos, and tutorials that will deepen your understanding of each topic.
Getting Started
1. Setting Up Your Environment
To get started with the code, you’ll need a Python environment with essential libraries like NumPy, Pandas, scikit-learn, and Matplotlib. We recommend using Anaconda or Jupyter Notebook for a smooth experience.
2. Topics Covered
This repository covers a variety of AI/ML topics, such as:
  • Data Preprocessing: Learn to clean and prepare data for ML models.
  • Model Tuning: Learn to optimize hyperparameters to improve a machine learning model's performance.
  • Supervised Learning: Implementation of algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines.
  • Unsupervised Learning: Explore Clustering techniques like K-Means, Hierarchical Clustering, and Dimensionality Reduction.
  • Neural Networks and Deep Learning: Introduction to concepts like Perceptrons, Feedforward Networks, and Convolutional Neural Networks.
  • Deep Computer Vision: Introduction to Object Detection, Image Segmentation and Image Classification.
  • Natural Language Processing (NLP): Basics of text processing, feature extraction, and text classification.
  • Model Evaluation: Techniques for evaluating model performance, such as confusion matrices, accuracy, and precision-recall.
3. Learning Path
Each folder is structured to build on the previous one. We recommend going through the folders in sequence for a smooth learning experience.
Resources and Additional Reading
Each folder contains a README file with carefully selected resources, including:
  • Articles: Introductory and advanced articles to understand the theoretical background.
  • Videos: Visual aids to reinforce the concepts.
  • Notebooks: Links to other example notebooks and code snippets to enhance your understanding.
Contributions
We encourage contributions! If you have ideas for additional resources, improvements to existing notebooks, or new algorithms to add, feel free to open a pull request or submit an issue. All contributions are welcome to make this repository more valuable for everyone.
Support
For any questions, feel free to reach out through the issue tracker or directly through our workshop's communication channels. We’re here to help you on your journey to mastering AI and ML!
Happy learning and coding!
About
This repository will contain all the codes discussed and displayed in the AI workshops
Resources
Readme
Activity
Custom properties
Stars
16 stars
Watchers
0 watching
Forks
13 forks
Report repository
Releases
Packages
Used by
Contributors
Languages