Unsupervised learning

As explained in Investing for Programmers

Unsupervised learning is a machine-learning approach that identifies structure or patterns in data without predefined output labels.

How Investing for Programmers covers it

How deep it goes

Defines structure discovery and demonstrates feature calculation, elbow selection, K-means clustering, plotting, and cautious interpretation.

Examples

Calculates stock returns and volatility, selects five clusters, plots S&P 500 assets, and examines Moderna, Palantir, and Super Micro Computer.

What you can do after

Apply K-means with pandas, yfinance, and interactive plots to categorize assets and identify candidates for further investigation.

How it connects

Connects clustering with machine learning, supervised learning, classification, regression, features, and outlier detection.

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Also explained in

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  Data Without LabelsAn unsupervised deep learning algorithm trains a multilayer neural model to discover structure or representations without target labels.
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  Grokking Machine LearningA machine-learning approach in which a model discovers structure or patterns from data without target outputs.
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  Financial AI in PracticeUnsupervised learning is a machine-learning approach that identifies structure or patterns in data without relying on labeled outcomes.
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  Optimization Algorithms: AI techniques for design, planning, and control problemsUnsupervised learning is a machine-learning paradigm in which a model discovers structure or patterns in data without labeled target outputs.
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  Inside AIUnsupervised learning is a machine-learning technique in which an algorithm analyzes unlabeled data to discover patterns or groupings.
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  Cyber Threat HuntingUnsupervised machine learning is a machine-learning paradigm that discovers patterns and structures in data without predefined labels or specific instructions.
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  Statistics Slam DunkUnsupervised learning is a machine-learning approach that discovers structure or patterns in data without labeled target values.
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  Outlier Detection in PythonMachine learning that extracts structure, information, or patterns from data without labeled target values.
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  The AI Pocket BookA machine learning approach that seeks patterns in unlabeled data without a target output.
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  Machine Learning Algorithms in DepthUnsupervised learning is a machine learning paradigm that learns structure or representations from data without labeled target outputs.
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  Natural Language Processing in Action, Second EditionUnsupervised learning is a machine-learning approach that derives patterns or representations directly from data without human-provided target labels.
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  Grokking AI ApplicationsUnsupervised learning is a machine learning approach that discovers patterns or structure in data without labeled examples.
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  Math and Architectures of Deep LearningA learning setup that uses training inputs without manually provided target outputs.
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  Acing the CCNA Exam: Volume 2 Advanced Networking and SecurityUnsupervised learning is a machine-learning approach that discovers patterns or structure in data without predefined labels.
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  Introduction to Generative AI, Second EditionUnsupervised learning is machine learning in which training data are provided without labels.
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  Inside Deep Learning: Math, Algorithms, ModelsA learning setting in which models are trained without human-provided labels, often by exploiting structure in the data.
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  The Art of AI Product DevelopmentUnsupervised learning is machine learning in which a model learns patterns from data without explicitly provided target labels.
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  Advanced Algorithms and Data StructuresUnsupervised learning is machine learning that analyzes unlabeled data to discover structure or patterns without predefined categories.
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  Software Testing with Generative AIA machine-learning approach that trains a model on unlabeled data to discover implicit patterns or structure.

Compare how 20 books explain unsupervised learning →

Related concepts

• supervised learningcontrasts with · Supervised learning is a machine-learning approach that learns to map inputs to target outputs from examples paired with known labels or values.
• K-means clusteringincludes · K-means clustering partitions data into a specified number of groups by assigning observations to representative centers and iteratively updating those centers to reduce within-group distances.
• density estimatorincludes · A density estimator is a statistical method or model that estimates the probability density underlying observed data.
• hierarchical clusteringincludes · Hierarchical clustering is a clustering method that organizes observations into nested groups by successively merging or splitting clusters.
• machine learningkind of · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
• clusteringincludes · Clustering is the technique of grouping data items according to similarity or shared characteristics.

This explainer was generated by Manning's knowledge graph from the text of Investing for Programmers. The section links point to the parts of the book it draws on.