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Apply what you've learned with these interactive coding exercises.

Deep Learning

Implement Stochastic Gradient Descent with Weight Decay and Gradient Accumulation

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Clustering Algorithms

K-means with Convergence Checking

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Statistical Techniques in ML

Compute Confidence Intervals for ML Evaluation Results Using Bootstrap

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Tree-based Models

Implement Decision Tree Classifier with Multiple Pre-Pruning Techniques

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Probability

Sampling for Stream Data

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Linear Algebra

Matrix Determinant Calculation

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Linear Algebra

QR Decomposition Implementation

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Data Processing

Implement Training Data Iterator with Class Balancing

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