MLG 012 Shallow Algos 1

Machine Learning Guide

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8y ago 53:36

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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

57 episodes

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Machining #Learn

MLG 012 Shallow Algos 1

Machine Learning Guide

591 subscribers

published 8y ago

MP3•Episode home

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

57 episodes

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Machining #Learn

All episodes

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