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Add Quick Reference: Loss Functions and Feature Engineering sections
- Added Section 16: Quick Reference: Loss Functions - Regression Losses (MSE, MAE, Huber) - Classification Losses (Binary Cross-Entropy, Categorical Cross-Entropy, Hinge) - Added Section 17: Feature Engineering - Normalization Techniques (Min-Max, Z-Score, Max Abs) - Polynomial Features (Degree 2 & 3) - Encoding Categorical Variables (One-Hot, Label) - Updated navigation with 2 new section buttons - Updated hero stats to show "17 Topics"
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‎index.html‎

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<h1>📊 Mathematics for Data Science & Machine Learning</h1>
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<p>A comprehensive, beautifully rendered reference for formulas and concepts.</p>
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<div class="hero-stats" aria-hidden="true">
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<div class="stat-box"><span class="stat-number">15</span><span class="stat-label">Topics</span></div>
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<div class="stat-box"><span class="stat-number">17</span><span class="stat-label">Topics</span></div>
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<div class="stat-box"><span class="stat-number">200+</span><span class="stat-label">Formulas</span></div>
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<div class="stat-box"><span class="stat-number">∞</span><span class="stat-label">Applications</span></div>
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</div>
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<button class="nav-btn" onclick="scrollToSection('svm')">SVM</button>
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<button class="nav-btn" onclick="scrollToSection('decision-trees')">Decision Trees</button>
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<button class="nav-btn" onclick="scrollToSection('bias-variance')">Bias-Variance</button>
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<button class="nav-btn" onclick="scrollToSection('loss-functions')">Loss Functions</button>
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<button class="nav-btn" onclick="scrollToSection('feature-engineering')">Feature Eng.</button>
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</div>
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<div class="expand-collapse-all">
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<button class="expand-collapse-btn" onclick="expandAllSections()">📂 Expand All</button>
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</div>
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</section>
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<!-- 16. QUICK REFERENCE: LOSS FUNCTIONS -->
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<section id="loss-functions" class="section" aria-labelledby="loss-title">
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<div class="section-header" onclick="toggleSection(this)">
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<div class="section-number">16</div>
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<h2 id="loss-title" class="section-title">Quick Reference: Loss Functions</h2>
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<div class="section-toggle">▼</div>
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</div>
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<div class="section-content">
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<div class="subsection">
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<div class="subsection-title">📉 Regression Losses</div>
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<table class="formula-table">
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<tr><th>Loss</th><th>Formula</th><th>Use Case</th></tr>
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<tr><td>MSE</td><td>$$L=\frac{1}{n}\sum_{i=1}^n(y_i-\hat{y}_i)^2$$</td><td>Standard regression</td></tr>
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<tr><td>MAE</td><td>$$L=\frac{1}{n}\sum_{i=1}^n|y_i-\hat{y}_i|$$</td><td>Robust to outliers</td></tr>
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<tr><td>Huber</td><td>$$L=\begin{cases}\frac{1}{2}(y-\hat{y})^2&|y-\hat{y}|\leq\delta\\\delta|y-\hat{y}|-\frac{1}{2}\delta^2&\text{otherwise}\end{cases}$$</td><td>Combines MSE & MAE</td></tr>
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</table>
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</div>
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<div class="subsection">
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<div class="subsection-title">🎯 Classification Losses</div>
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<table class="formula-table">
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<tr><th>Loss</th><th>Formula</th><th>Use Case</th></tr>
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<tr><td>Binary Cross-Entropy</td><td>$$L=-[y\log(\hat{y})+(1-y)\log(1-\hat{y})]$$</td><td>Binary classification</td></tr>
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<tr><td>Categorical Cross-Entropy</td><td>$$L=-\sum_i y_i\log(\hat{y}_i)$$</td><td>Multi-class classification</td></tr>
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<tr><td>Hinge Loss</td><td>$$L=\max(0,1-y\cdot\hat{y})$$</td><td>SVM classification</td></tr>
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</table>
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</div>
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</div>
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</section>
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<!-- 17. FEATURE ENGINEERING -->
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<section id="feature-engineering" class="section" aria-labelledby="fe-title">
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<div class="section-header" onclick="toggleSection(this)">
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<div class="section-number">17</div>
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<h2 id="fe-title" class="section-title">Feature Engineering</h2>
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<div class="section-toggle">▼</div>
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</div>
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<div class="section-content">
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<div class="subsection">
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<div class="subsection-title">📊 Normalization Techniques</div>
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<table class="formula-table">
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<tr><th>Method</th><th>Formula</th><th>Range</th></tr>
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<tr><td>Min-Max Scaling</td><td>$$x'=\frac{x-\min}{\max-\min}$$</td><td>[0, 1]</td></tr>
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<tr><td>Z-Score Normalization</td><td>$$x'=\frac{x-\mu}{\sigma}$$</td><td>~ [-3, 3]</td></tr>
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<tr><td>Max Abs Scaling</td><td>$$x'=\frac{x}{|\max|}$$</td><td>[-1, 1]</td></tr>
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</table>
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</div>
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<div class="subsection">
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<div class="subsection-title">🔢 Polynomial Features</div>
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<div class="formula-card">
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<div class="formula-name">Degree 2</div>
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<div class="formula">$$[x_1, x_2] \rightarrow [1, x_1, x_2, x_1^2, x_1 x_2, x_2^2]$$</div>
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</div>
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<div class="formula-card">
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<div class="formula-name">Degree 3</div>
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<div class="formula">$$[x_1, x_2] \rightarrow [1, x_1, x_2, x_1^2, x_1 x_2, x_2^2, x_1^3, x_1^2 x_2, x_1 x_2^2, x_2^3]$$</div>
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</div>
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<div class="info-box">
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<strong>💡 Tip:</strong> Polynomial features help capture non-linear relationships in data, but be careful of overfitting with high degrees.
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</div>
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</div>
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<div class="subsection">
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<div class="subsection-title">🏷️ Encoding Categorical Variables</div>
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<div class="formula-card">
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<div class="formula-name">One-Hot Encoding</div>
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<div class="formula">$$\text{Category } c \rightarrow [0, 0, \ldots, 1, \ldots, 0] \text{ (1 at position } c\text{)}$$</div>
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</div>
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<div class="formula-card">
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<div class="formula-name">Label Encoding</div>
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<div class="formula">$$\text{Categories } \{A, B, C\} \rightarrow \{0, 1, 2\}$$</div>
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</div>
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</div>
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</div>
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</section>
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<!-- CONTRIBUTOR SECTION -->
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<section id="contributor" class="section" style="text-align:center;">
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<div class="section-header" onclick="toggleSection(this)">

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