Applied / Practical ML — learning path · Lensa ML

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Applied / Practical ML

Feature pipelines, embeddings, transfer learning, and dimensionality reduction

1
Feature Extraction
How networks find patterns in raw data
2
Data Preprocessing
Cleaning, scaling, and encoding raw data
3
Embeddings
Turning words and categories into vectors
4
Transfer Learning
Standing on the shoulders of pretrained giants
5
Dimensionality Reduction
PCA and t-SNE — seeing high-dimensional data

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Neural networks
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