Spaces:
Running
Running
undefined - Initial Deployment
Browse files- README.md +7 -5
- index.html +490 -19
README.md
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---
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title:
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emoji:
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colorFrom:
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colorTo:
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sdk: static
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: ml-pipleline
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emoji: 🐳
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colorFrom: green
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colorTo: yellow
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sdk: static
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pinned: false
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tags:
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- deepsite
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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index.html
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<html>
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| 1 |
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<!DOCTYPE html>
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| 2 |
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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| 5 |
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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| 6 |
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<title>ML Pipeline Pro | From Data to Deployment</title>
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| 7 |
+
<script src="https://cdn.tailwindcss.com"></script>
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| 8 |
+
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
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| 9 |
+
<style>
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| 10 |
+
.gradient-bg {
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| 11 |
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background: linear-gradient(135deg, #6e8efb, #a777e3);
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| 12 |
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}
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| 13 |
+
.pipeline-step {
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| 14 |
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transition: all 0.3s ease;
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| 15 |
+
}
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| 16 |
+
.pipeline-step:hover {
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| 17 |
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transform: translateY(-5px);
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| 18 |
+
box-shadow: 0 10px 25px rgba(0,0,0,0.1);
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| 19 |
+
}
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| 20 |
+
.code-block {
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| 21 |
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font-family: 'Courier New', monospace;
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| 22 |
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background-color: #2d3748;
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| 23 |
+
color: #f7fafc;
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| 24 |
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}
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| 25 |
+
.animated-arrow {
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| 26 |
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animation: bounce 2s infinite;
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| 27 |
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}
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| 28 |
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@keyframes bounce {
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| 29 |
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0%, 20%, 50%, 80%, 100% {transform: translateX(0);}
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| 30 |
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40% {transform: translateX(10px);}
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| 31 |
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60% {transform: translateX(5px);}
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}
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</style>
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| 34 |
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</head>
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| 35 |
+
<body class="bg-gray-50 font-sans">
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| 36 |
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<!-- Navigation -->
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| 37 |
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<nav class="bg-white shadow-lg">
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| 38 |
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<div class="max-w-6xl mx-auto px-4">
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| 39 |
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<div class="flex justify-between items-center py-4">
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| 40 |
+
<div class="flex items-center space-x-4">
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| 41 |
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<div class="flex items-center">
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| 42 |
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<i class="fas fa-robot text-2xl text-purple-600 mr-2"></i>
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| 43 |
+
<span class="font-bold text-xl text-gray-800">ML Pipeline Pro</span>
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| 44 |
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</div>
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| 45 |
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</div>
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| 46 |
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<div class="hidden md:flex items-center space-x-8">
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| 47 |
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<a href="#features" class="text-gray-700 hover:text-purple-600">Features</a>
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| 48 |
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<a href="#pipeline" class="text-gray-700 hover:text-purple-600">Pipeline</a>
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| 49 |
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<a href="#resources" class="text-gray-700 hover:text-purple-600">Resources</a>
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| 50 |
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<a href="#contact" class="text-gray-700 hover:text-purple-600">Contact</a>
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| 51 |
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<button class="bg-purple-600 text-white px-4 py-2 rounded-md hover:bg-purple-700 transition">Get Started</button>
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| 52 |
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</div>
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| 53 |
+
<div class="md:hidden">
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| 54 |
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<button class="text-gray-700 focus:outline-none">
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| 55 |
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<i class="fas fa-bars text-2xl"></i>
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| 56 |
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</button>
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| 57 |
+
</div>
|
| 58 |
+
</div>
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| 59 |
+
</div>
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| 60 |
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</nav>
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| 61 |
+
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| 62 |
+
<!-- Hero Section -->
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| 63 |
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<section class="gradient-bg text-white py-20">
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| 64 |
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<div class="max-w-6xl mx-auto px-4 sm:px-6 lg:px-8">
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| 65 |
+
<div class="flex flex-col md:flex-row items-center">
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| 66 |
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<div class="md:w-1/2 mb-10 md:mb-0">
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| 67 |
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<h1 class="text-4xl md:text-5xl font-bold mb-6">Your Complete ML Pipeline Solution</h1>
|
| 68 |
+
<p class="text-xl mb-8">From data collection to model deployment, we guide you through every step of the machine learning process with expert tools and resources.</p>
|
| 69 |
+
<div class="flex flex-col sm:flex-row space-y-4 sm:space-y-0 sm:space-x-4">
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| 70 |
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<button class="bg-white text-purple-600 px-6 py-3 rounded-lg font-semibold hover:bg-gray-100 transition">Start Your Project</button>
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| 71 |
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<button class="border-2 border-white text-white px-6 py-3 rounded-lg font-semibold hover:bg-white hover:text-purple-600 transition">Learn More</button>
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| 72 |
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</div>
|
| 73 |
+
</div>
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| 74 |
+
<div class="md:w-1/2 flex justify-center">
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| 75 |
+
<img src="https://illustrations.popsy.co/amber/artificial-intelligence.svg" alt="ML Illustration" class="w-4/5">
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| 76 |
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</div>
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| 77 |
+
</div>
|
| 78 |
+
</div>
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| 79 |
+
</section>
|
| 80 |
+
|
| 81 |
+
<!-- Features Section -->
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| 82 |
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<section id="features" class="py-16 bg-white">
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| 83 |
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<div class="max-w-6xl mx-auto px-4 sm:px-6 lg:px-8">
|
| 84 |
+
<div class="text-center mb-16">
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| 85 |
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<h2 class="text-3xl font-bold text-gray-800 mb-4">Powerful Features for Your ML Journey</h2>
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| 86 |
+
<p class="text-xl text-gray-600 max-w-3xl mx-auto">Everything you need to build, train, and deploy machine learning models efficiently</p>
|
| 87 |
+
</div>
|
| 88 |
+
<div class="grid grid-cols-1 md:grid-cols-3 gap-8">
|
| 89 |
+
<div class="bg-gray-50 p-8 rounded-xl shadow-md hover:shadow-xl transition pipeline-step">
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| 90 |
+
<div class="text-purple-600 mb-4">
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| 91 |
+
<i class="fas fa-database text-4xl"></i>
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| 92 |
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</div>
|
| 93 |
+
<h3 class="text-xl font-semibold mb-3">Data Management</h3>
|
| 94 |
+
<p class="text-gray-600">Automated data collection, cleaning, and preprocessing tools to prepare your datasets for modeling.</p>
|
| 95 |
+
</div>
|
| 96 |
+
<div class="bg-gray-50 p-8 rounded-xl shadow-md hover:shadow-xl transition pipeline-step">
|
| 97 |
+
<div class="text-purple-600 mb-4">
|
| 98 |
+
<i class="fas fa-brain text-4xl"></i>
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| 99 |
+
</div>
|
| 100 |
+
<h3 class="text-xl font-semibold mb-3">Model Training</h3>
|
| 101 |
+
<p class="text-gray-600">Intuitive interfaces for model selection, hyperparameter tuning, and training with real-time monitoring.</p>
|
| 102 |
+
</div>
|
| 103 |
+
<div class="bg-gray-50 p-8 rounded-xl shadow-md hover:shadow-xl transition pipeline-step">
|
| 104 |
+
<div class="text-purple-600 mb-4">
|
| 105 |
+
<i class="fas fa-rocket text-4xl"></i>
|
| 106 |
+
</div>
|
| 107 |
+
<h3 class="text-xl font-semibold mb-3">Deployment</h3>
|
| 108 |
+
<p class="text-gray-600">One-click deployment to various platforms with monitoring and scaling capabilities built-in.</p>
|
| 109 |
+
</div>
|
| 110 |
+
</div>
|
| 111 |
+
</div>
|
| 112 |
+
</section>
|
| 113 |
+
|
| 114 |
+
<!-- Pipeline Steps Section -->
|
| 115 |
+
<section id="pipeline" class="py-16 bg-gray-50">
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| 116 |
+
<div class="max-w-6xl mx-auto px-4 sm:px-6 lg:px-8">
|
| 117 |
+
<div class="text-center mb-16">
|
| 118 |
+
<h2 class="text-3xl font-bold text-gray-800 mb-4">The Complete ML Pipeline</h2>
|
| 119 |
+
<p class="text-xl text-gray-600 max-w-3xl mx-auto">Follow our step-by-step guide to navigate through your machine learning project</p>
|
| 120 |
+
</div>
|
| 121 |
+
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<!-- Pipeline Visualization -->
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<div class="relative">
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<!-- Progress line -->
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<div class="hidden md:block absolute left-8 top-0 h-full w-1 bg-purple-200 ml-4"></div>
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<div class="relative mb-12">
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<div class="flex flex-col md:flex-row items-center">
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<div class="bg-white p-6 rounded-lg shadow-md pipeline-step">
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<h3 class="text-xl font-semibold mb-3 text-purple-600">Data Collection</h3>
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<p class="text-gray-600 mb-4">Gather your raw data from various sources including databases, APIs, or files.</p>
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| 137 |
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<div class="code-block p-4 rounded-md mb-4">
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<code class="text-sm">
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# Python example: Loading data from CSV<br>
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import pandas as pd<br>
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data = pd.read_csv('dataset.csv')
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</code>
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</div>
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<button class="text-purple-600 font-medium hover:underline">Explore Data Sources</button>
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<div class="flex items-center justify-center w-16 h-16 rounded-full bg-purple-600 text-white text-xl font-bold mb-4 md:mb-0 z-10">
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<div class="bg-white p-6 rounded-lg shadow-md pipeline-step">
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<h3 class="text-xl font-semibold mb-3 text-purple-600">Data Preprocessing</h3>
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<p class="text-gray-600 mb-4">Clean and transform your data to make it suitable for modeling.</p>
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<div class="code-block p-4 rounded-md mb-4">
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<code class="text-sm">
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# Handling missing values<br>
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data = data.fillna(data.mean())<br><br>
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# Feature scaling<br>
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from sklearn.preprocessing import StandardScaler<br>
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scaler = StandardScaler()<br>
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scaled_data = scaler.fit_transform(data)
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</code>
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</div>
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<button class="text-purple-600 font-medium hover:underline">Preprocessing Tools</button>
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</div>
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</div>
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</div>
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</div>
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3
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</div>
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<div class="md:ml-8 w-full md:w-auto">
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<div class="bg-white p-6 rounded-lg shadow-md pipeline-step">
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<h3 class="text-xl font-semibold mb-3 text-purple-600">Feature Engineering</h3>
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<p class="text-gray-600 mb-4">Create meaningful features that will help your model make better predictions.</p>
|
| 186 |
+
<div class="code-block p-4 rounded-md mb-4">
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<code class="text-sm">
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| 188 |
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# Creating interaction features<br>
|
| 189 |
+
data['feature_interaction'] = data['feat1'] * data['feat2']<br><br>
|
| 190 |
+
# One-hot encoding<br>
|
| 191 |
+
data = pd.get_dummies(data, columns=['categorical_feature'])
|
| 192 |
+
</code>
|
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</div>
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<button class="text-purple-600 font-medium hover:underline">Feature Techniques</button>
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</div>
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</div>
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</div>
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<!-- Step 4 -->
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<div class="relative mb-12">
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<div class="flex flex-col md:flex-row items-center">
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<div class="flex items-center justify-center w-16 h-16 rounded-full bg-purple-600 text-white text-xl font-bold mb-4 md:mb-0 z-10">
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4
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</div>
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<div class="md:ml-8 w-full md:w-auto">
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<div class="bg-white p-6 rounded-lg shadow-md pipeline-step">
|
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<h3 class="text-xl font-semibold mb-3 text-purple-600">Model Selection</h3>
|
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<p class="text-gray-600 mb-4">Choose the right algorithm for your problem type and data characteristics.</p>
|
| 210 |
+
<div class="flex flex-wrap gap-4 mb-4">
|
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+
<span class="bg-purple-100 text-purple-800 px-3 py-1 rounded-full text-sm">Linear Regression</span>
|
| 212 |
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<span class="bg-purple-100 text-purple-800 px-3 py-1 rounded-full text-sm">Random Forest</span>
|
| 213 |
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<span class="bg-purple-100 text-purple-800 px-3 py-1 rounded-full text-sm">Neural Networks</span>
|
| 214 |
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<span class="bg-purple-100 text-purple-800 px-3 py-1 rounded-full text-sm">SVM</span>
|
| 215 |
+
</div>
|
| 216 |
+
<button class="text-purple-600 font-medium hover:underline">Algorithm Guide</button>
|
| 217 |
+
</div>
|
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+
</div>
|
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+
</div>
|
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</div>
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+
|
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+
<!-- Step 5 -->
|
| 223 |
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<div class="relative mb-12">
|
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+
<div class="flex flex-col md:flex-row items-center">
|
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<div class="flex items-center justify-center w-16 h-16 rounded-full bg-purple-600 text-white text-xl font-bold mb-4 md:mb-0 z-10">
|
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+
5
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</div>
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<div class="md:ml-8 w-full md:w-auto">
|
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<div class="bg-white p-6 rounded-lg shadow-md pipeline-step">
|
| 230 |
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<h3 class="text-xl font-semibold mb-3 text-purple-600">Model Training</h3>
|
| 231 |
+
<p class="text-gray-600 mb-4">Train your model on the prepared dataset and evaluate its performance.</p>
|
| 232 |
+
<div class="code-block p-4 rounded-md mb-4">
|
| 233 |
+
<code class="text-sm">
|
| 234 |
+
# Splitting data<br>
|
| 235 |
+
from sklearn.model_selection import train_test_split<br>
|
| 236 |
+
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)<br><br>
|
| 237 |
+
# Training a model<br>
|
| 238 |
+
from sklearn.ensemble import RandomForestClassifier<br>
|
| 239 |
+
model = RandomForestClassifier()<br>
|
| 240 |
+
model.fit(X_train, y_train)
|
| 241 |
+
</code>
|
| 242 |
+
</div>
|
| 243 |
+
<button class="text-purple-600 font-medium hover:underline">Training Options</button>
|
| 244 |
+
</div>
|
| 245 |
+
</div>
|
| 246 |
+
</div>
|
| 247 |
+
</div>
|
| 248 |
+
|
| 249 |
+
<!-- Step 6 -->
|
| 250 |
+
<div class="relative mb-12">
|
| 251 |
+
<div class="flex flex-col md:flex-row items-center">
|
| 252 |
+
<div class="flex items-center justify-center w-16 h-16 rounded-full bg-purple-600 text-white text-xl font-bold mb-4 md:mb-0 z-10">
|
| 253 |
+
6
|
| 254 |
+
</div>
|
| 255 |
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<div class="md:ml-8 w-full md:w-auto">
|
| 256 |
+
<div class="bg-white p-6 rounded-lg shadow-md pipeline-step">
|
| 257 |
+
<h3 class="text-xl font-semibold mb-3 text-purple-600">Model Evaluation</h3>
|
| 258 |
+
<p class="text-gray-600 mb-4">Assess your model's performance using appropriate metrics.</p>
|
| 259 |
+
<div class="code-block p-4 rounded-md mb-4">
|
| 260 |
+
<code class="text-sm">
|
| 261 |
+
# Evaluation metrics<br>
|
| 262 |
+
from sklearn.metrics import accuracy_score, precision_score, recall_score<br>
|
| 263 |
+
predictions = model.predict(X_test)<br><br>
|
| 264 |
+
print("Accuracy:", accuracy_score(y_test, predictions))<br>
|
| 265 |
+
print("Precision:", precision_score(y_test, predictions))<br>
|
| 266 |
+
print("Recall:", recall_score(y_test, predictions))
|
| 267 |
+
</code>
|
| 268 |
+
</div>
|
| 269 |
+
<div class="flex items-center text-purple-600">
|
| 270 |
+
<span>View Evaluation Dashboard</span>
|
| 271 |
+
<i class="fas fa-arrow-right ml-2 animated-arrow"></i>
|
| 272 |
+
</div>
|
| 273 |
+
</div>
|
| 274 |
+
</div>
|
| 275 |
+
</div>
|
| 276 |
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</div>
|
| 277 |
+
|
| 278 |
+
<!-- Step 7 -->
|
| 279 |
+
<div class="relative">
|
| 280 |
+
<div class="flex flex-col md:flex-row items-center">
|
| 281 |
+
<div class="flex items-center justify-center w-16 h-16 rounded-full bg-purple-600 text-white text-xl font-bold mb-4 md:mb-0 z-10">
|
| 282 |
+
7
|
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+
</div>
|
| 284 |
+
<div class="md:ml-8 w-full md:w-auto">
|
| 285 |
+
<div class="bg-white p-6 rounded-lg shadow-md pipeline-step">
|
| 286 |
+
<h3 class="text-xl font-semibold mb-3 text-purple-600">Model Deployment</h3>
|
| 287 |
+
<p class="text-gray-600 mb-4">Deploy your trained model to production for real-world use.</p>
|
| 288 |
+
<div class="code-block p-4 rounded-md mb-4">
|
| 289 |
+
<code class="text-sm">
|
| 290 |
+
# Saving the model<br>
|
| 291 |
+
import joblib<br>
|
| 292 |
+
joblib.dump(model, 'model.pkl')<br><br>
|
| 293 |
+
# Flask API example<br>
|
| 294 |
+
from flask import Flask, request, jsonify<br>
|
| 295 |
+
app = Flask(__name__)<br>
|
| 296 |
+
@app.route('/predict', methods=['POST'])<br>
|
| 297 |
+
def predict():<br>
|
| 298 |
+
data = request.json<br>
|
| 299 |
+
prediction = model.predict([data['features']])<br>
|
| 300 |
+
return jsonify({'prediction': prediction.tolist()})
|
| 301 |
+
</code>
|
| 302 |
+
</div>
|
| 303 |
+
<button class="bg-purple-600 text-white px-4 py-2 rounded-md hover:bg-purple-700 transition">Deploy Now</button>
|
| 304 |
+
</div>
|
| 305 |
+
</div>
|
| 306 |
+
</div>
|
| 307 |
+
</div>
|
| 308 |
+
</div>
|
| 309 |
+
</div>
|
| 310 |
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</section>
|
| 311 |
+
|
| 312 |
+
<!-- Resources Section -->
|
| 313 |
+
<section id="resources" class="py-16 bg-white">
|
| 314 |
+
<div class="max-w-6xl mx-auto px-4 sm:px-6 lg:px-8">
|
| 315 |
+
<div class="text-center mb-16">
|
| 316 |
+
<h2 class="text-3xl font-bold text-gray-800 mb-4">Learning Resources</h2>
|
| 317 |
+
<p class="text-xl text-gray-600 max-w-3xl mx-auto">Expand your machine learning knowledge with our curated resources</p>
|
| 318 |
+
</div>
|
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<div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-8">
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| 320 |
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</div>
|
| 324 |
+
<div class="p-6">
|
| 325 |
+
<h3 class="text-xl font-semibold mb-2">ML Fundamentals</h3>
|
| 326 |
+
<p class="text-gray-600 mb-4">Learn the core concepts of machine learning with our beginner-friendly guide.</p>
|
| 327 |
+
<a href="#" class="text-purple-600 font-medium hover:underline">Start Learning</a>
|
| 328 |
+
</div>
|
| 329 |
+
</div>
|
| 330 |
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<div class="border border-gray-200 rounded-lg overflow-hidden shadow-sm hover:shadow-md transition pipeline-step">
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| 332 |
+
<i class="fas fa-laptop-code text-5xl text-purple-600"></i>
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| 333 |
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| 334 |
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<div class="p-6">
|
| 335 |
+
<h3 class="text-xl font-semibold mb-2">Hands-on Tutorials</h3>
|
| 336 |
+
<p class="text-gray-600 mb-4">Step-by-step tutorials for real-world ML projects across different domains.</p>
|
| 337 |
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<a href="#" class="text-purple-600 font-medium hover:underline">Explore Tutorials</a>
|
| 338 |
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|
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<div class="border border-gray-200 rounded-lg overflow-hidden shadow-sm hover:shadow-md transition pipeline-step">
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|
| 344 |
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<div class="p-6">
|
| 345 |
+
<h3 class="text-xl font-semibold mb-2">Advanced Techniques</h3>
|
| 346 |
+
<p class="text-gray-600 mb-4">Dive deep into advanced ML concepts and cutting-edge research.</p>
|
| 347 |
+
<a href="#" class="text-purple-600 font-medium hover:underline">Advance Your Skills</a>
|
| 348 |
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| 350 |
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|
| 357 |
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<h2 class="text-3xl md:text-4xl font-bold mb-6">Ready to Build Your ML Pipeline?</h2>
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