Audio is streamed directly from the publisher (traffic.libsyn.com) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.
Show Notes
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.
- 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