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Name,StudyHours,Grade | ||
Arun,10,50 | ||
Sofia,11.5,50 | ||
Hassan,9,47 | ||
Zara,16,97 | ||
Liam,9.25,49 | ||
Xia,1,3 | ||
Carlos,11.5,53 | ||
Kwame,9,42 | ||
Fatima,8.5,26 | ||
Takumi,14.5,74 | ||
Leila,15.5,82 | ||
Jin,13.75,62 | ||
Priya,9,37 | ||
Mei,8,15 | ||
Alberto,15.5,70 | ||
Anika,8,27 | ||
Elif,9,36 | ||
Tariq,6,35 | ||
Lakshmi,10,48 | ||
Maya,12,52 | ||
Yusuf,12.5,63 | ||
Zainab,12,64 | ||
Juan,8, | ||
Ali,, |
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# Binary Classification |
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# Classification |
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# Multi-class Classification |
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# Clustering |
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docs/notes/predictive-modeling/dimensionality-reduction/pca.qmd
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# Dimensionality Reduction | ||
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## Principal Component Analysis (PCA) |
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# Predictive Modeling in Python (for Finance) |
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docs/notes/predictive-modeling/ml-foundations/data-encoding.qmd
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# Data Encoding | ||
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## Ordinal Encoding | ||
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## One-hot Encoding |
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docs/notes/predictive-modeling/ml-foundations/generalization.qmd
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# Generalization | ||
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overfit vs underfit, | ||
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## Train-Test Split | ||
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## Cross Validation |
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# Machine Learning Foundations |
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docs/notes/predictive-modeling/ml-foundations/model-evaluation.qmd
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# Model Evaluation |
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docs/notes/predictive-modeling/ml-foundations/training-and-loss.qmd
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# Model Training and Evaluation | ||
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maximizing some objective, and measuring it. | ||
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Some models are "closed-form" models, which use a predetermined function to arrive at the ideal weights. The Linear Regression is one example of this. | ||
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Other models use an "iterative optimization" technique to arrive at the ideal weights over a number of simulated trial periods, or "epochs". Deep learning models rely on this iterative optimization approach. | ||
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## Model Evaluation | ||
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different metrics for regression vs classification | ||
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### Regression Metrics | ||
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### Classification Metrics |
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# Deploying Machine Learning Models |
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# Model Management |
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docs/notes/predictive-modeling/model-management/saving-loading.qmd
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# Saving and Loading Models | ||
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## Saving Trained Models | ||
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## Loading Pre-trained Models |
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docs/notes/predictive-modeling/model-optimization/grid-search.qmd
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# Model Optimization | ||
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## Hyperparameter Tuning | ||
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### Grid Search |
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docs/notes/predictive-modeling/regression/autoregressive-models.qmd
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# Autocorrelation and Auto-Regressive Models | ||
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## Autocorrelation | ||
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## Auto-Regressive Models |
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# Regression |
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# Linear Regression |
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# Advanced Regression Models | ||
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## Ridge | ||
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## Lasso |
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docs/notes/predictive-modeling/regression/time-series-forecasting.qmd
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# Regression for Time Series Forecasting |
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# Supervised Learning |
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# Unsupervised Learning |