MLG 012 Shallow Algos 1

Mar 19, 2017
Click to Play Episode

Shallow learning algorithms including K Nearest Neighbors, K Means, and decision trees. Supervised, unsupervised, and reinforcement learning methods for practical machine learning applications.

Resources
Resources best viewed here
Loading...
Show Notes
CTA
Learn Faster with a Walking DeskWalk While You Learn

Sitting for hours drains energy and focus. A walking desk boosts alertness, helping you retain complex ML topics more effectively.Boost focus and energy to learn faster and retain more.Discover the benefitsDiscover the benefits

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