MLG 012 Shallow Algos 1

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Shallow vs. Deep Learning: Shallow learning can often solve problems more efficiently in time and resources compared to deep learning.
Supervised Learning: Key algorithms include linear regression, logistic regression, neural networks, and K Nearest Neighbors (KNN). KNN is unique as it is instance-based and simple, categorizing new data based on proximity to known data points.
Unsupervised Learning:
- Clustering (K Means): Differentiates data points into clusters with no predefined labels, essential for discovering data structures without explicit supervision.
- Association Rule Learning: Example includes the a priori algorithm, which deduces the likelihood of item co-occurrence, commonly used in market basket analysis.
- Dimensionality Reduction (PCA): Condenses features into simplified forms, maintaining the essence of the data, crucial for managing high-dimensional datasets.
Decision Trees: Utilized for both classification and regression, decision trees offer a visible, understandable model structure. Variants like Random Forests and Gradient Boosting Trees increase performance and reduce overfitting risks.

Shallow vs. Deep Learning: Shallow learning can often solve problems more efficiently in time and resources compared to deep learning.
Supervised Learning: Key algorithms include linear regression, logistic regression, neural networks, and K Nearest Neighbors (KNN). KNN is unique as it is instance-based and simple, categorizing new data based on proximity to known data points.
Unsupervised Learning:
- Clustering (K Means): Differentiates data points into clusters with no predefined labels, essential for discovering data structures without explicit supervision.
- Association Rule Learning: Example includes the a priori algorithm, which deduces the likelihood of item co-occurrence, commonly used in market basket analysis.
- Dimensionality Reduction (PCA): Condenses features into simplified forms, maintaining the essence of the data, crucial for managing high-dimensional datasets.
Decision Trees: Utilized for both classification and regression, decision trees offer a visible, understandable model structure. Variants like Random Forests and Gradient Boosting Trees increase performance and reduce overfitting risks.

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