MLG 012 Shallow Algos 1

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/12 Topics •

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. • Links • Focus material: Andrew Ng Week 8 • . A Tour of Machine Learning Algorithms • for a comprehensive overview. Scikit Learn image • : A decision tree infographic for selecting the appropriate algorithm based on your specific needs. Pros/cons table for various algorithms