Unsupervised - Revision history

BPeat at 12:01, 2 July 2023

2023-07-02T12:01:38Z

BPeat at 12:00, 2 July 2023

2023-07-02T12:00:21Z

BPeat at 10:34, 26 August 2021

2021-08-26T10:34:05Z

BPeat at 03:04, 4 May 2020

2020-05-04T03:04:47Z

BPeat at 20:07, 8 December 2019

2019-12-08T20:07:32Z

BPeat at 14:02, 8 December 2019

2019-12-08T14:02:29Z

BPeat at 13:56, 8 December 2019

2019-12-08T13:56:24Z

BPeat at 13:52, 8 December 2019

2019-12-08T13:52:44Z

BPeat at 10:31, 22 May 2019

2019-05-22T10:31:35Z

BPeat at 10:30, 22 May 2019

2019-05-22T10:30:46Z

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 |title=PRIMO.ai
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-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [http://www.youtube.com/results?search_query=Unsupervised+Machine+Learning+Deep YouTube search...]

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 [http://www.google.com/search?q=Unsupervised+deep+machine+learning+ML+artificial+intelligence ...Google search]
-* [[Learning Techniques]]
+* [[Supervised|Supervised Learning]] ... [[Semi-Supervised]] ... [[Self-Supervised]] ... [[Unsupervised]]
-** [[Supervised]] Learning
+* [[Learning Techniques]]
 * [http://en.wikipedia.org/wiki/Unsupervised_learning Unsupervised Learning | Wikipedia]

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 Clustering and density estimation may be performed to learn about the patterns in the data.
-Additional unsupervised methods may also be used, such as visualization that involves graphing or plotting data in different ways and projection methods that involves reducing the dimensionality of the data.
+Additional unsupervised methods may also be used, such as visualization that involves graphing or plotting data in different ways and [[Dimensional Reduction#Projection |Projection]] methods that involves reducing the dimensionality of the data.
 * [[Visualization]]: Unsupervised learning problem that involves creating plots of data.
-* Projection: Unsupervised learning problem that involves creating lower-dimensional representations of data.
+* [[Dimensional Reduction#Projection |Projection]]: Unsupervised learning problem that involves creating lower-dimensional representations of data.
-An example of a [[visualization]] technique would be a scatter plot matrix that creates one scatter plot of each pair of variables in the dataset. An example of a projection method would be [[Principal Component Analysis (PCA)]] that involves summarizing a dataset in terms of eigenvalues and eigenvectors, with linear dependencies removed. [http://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery]
+An example of a [[visualization]] technique would be a scatter plot matrix that creates one scatter plot of each pair of variables in the dataset. An example of a [[Dimensional Reduction#Projection |Projection]] method would be [[Principal Component Analysis (PCA)]] that involves summarizing a dataset in terms of eigenvalues and eigenvectors, with linear dependencies removed. [http://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery]
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 There are many types of unsupervised learning, although there are two main problems that are often encountered by a practitioner: they are clustering that involves finding groups in the data and density estimation that involves summarizing the distribution of data.
 * Clustering: Unsupervised learning problem that involves finding groups in data.
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 An example of a [[visualization]] technique would be a scatter plot matrix that creates one scatter plot of each pair of variables in the dataset. An example of a projection method would be [[Principal Component Analysis (PCA)]] that involves summarizing a dataset in terms of eigenvalues and eigenvectors, with linear dependencies removed. [http://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery]
 http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_3.jpg

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 [http://www.google.com/search?q=Unsupervised+deep+machine+learning+ML+artificial+intelligence ...Google search]
-* [[Supervised]] Learning
+* [[Learning Techniques]]
 In unsupervised learning, the algorithm goes through the data itself and tries to come up with meaningful results. The result might be, for example, a set of clusters of data points that could be related within each cluster. That works better when the clusters don’t overlap. [http://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld]

← Older revision		Revision as of 13:56, 8 December 2019
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	* Projection: Unsupervised learning problem that involves creating lower-dimensional representations of data.		* Projection: Unsupervised learning problem that involves creating lower-dimensional representations of data.

−	An example of a visualization technique would be a scatter plot matrix that creates one scatter plot of each pair of variables in the dataset. An example of a projection method would be Principal Component Analysis that involves summarizing a dataset in terms of eigenvalues and eigenvectors, with linear dependencies removed. [http://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning \| Jason Brownlee - Machine Learning Mastery]	+	An example of a [http://machinelearningmastery.com/data-visualization-methods-in-python/ visualization] technique would be a scatter plot matrix that creates one scatter plot of each pair of variables in the dataset. An example of a projection method would be [[Principal Component Analysis (PCA)]] that involves summarizing a dataset in terms of eigenvalues and eigenvectors, with linear dependencies removed. [http://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning \| Jason Brownlee - Machine Learning Mastery]

← Older revision		Revision as of 13:52, 8 December 2019
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	Unlike [[supervised]] learning, unsupervised learning is used with data sets without historical data. An unsupervised learning algorithm explores surpassed data to find the structure. This kind of learning works best for transactional data; for instance, it helps in identifying customer segments and clusters with certain attributes—this is often used in content personalization. Popular techniques where unsupervised learning is used also include self-organizing maps, nearest neighbor mappig, singular value decomposition, and [[K-Means]] [[clustering]]. Basically, online recommendations, identification of data outliers, and segment text topics are all examples of unsupervised learning. [http://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article Machine Learning: What it is and Why it Matters \| Priyadharshini @ simplilearn]		Unlike [[supervised]] learning, unsupervised learning is used with data sets without historical data. An unsupervised learning algorithm explores surpassed data to find the structure. This kind of learning works best for transactional data; for instance, it helps in identifying customer segments and clusters with certain attributes—this is often used in content personalization. Popular techniques where unsupervised learning is used also include self-organizing maps, nearest neighbor mappig, singular value decomposition, and [[K-Means]] [[clustering]]. Basically, online recommendations, identification of data outliers, and segment text topics are all examples of unsupervised learning. [http://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article Machine Learning: What it is and Why it Matters \| Priyadharshini @ simplilearn]
		+
		+	There are many types of unsupervised learning, although there are two main problems that are often encountered by a practitioner: they are clustering that involves finding groups in the data and density estimation that involves summarizing the distribution of data.
		+
		+	* Clustering: Unsupervised learning problem that involves finding groups in data.
		+	* Density Estimation: Unsupervised learning problem that involves summarizing the distribution of data.
		+
		+	Clustering and density estimation may be performed to learn about the patterns in the data.
		+
		+	Additional unsupervised methods may also be used, such as visualization that involves graphing or plotting data in different ways and projection methods that involves reducing the dimensionality of the data.
		+
		+	* Visualization: Unsupervised learning problem that involves creating plots of data.
		+	* Projection: Unsupervised learning problem that involves creating lower-dimensional representations of data.
		+
		+	An example of a visualization technique would be a scatter plot matrix that creates one scatter plot of each pair of variables in the dataset. An example of a projection method would be Principal Component Analysis that involves summarizing a dataset in terms of eigenvalues and eigenvectors, with linear dependencies removed. [http://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning \| Jason Brownlee - Machine Learning Mastery]
		+

	http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_3.jpg		http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_3.jpg

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 In unsupervised learning, the algorithm goes through the data itself and tries to come up with meaningful results. The result might be, for example, a set of clusters of data points that could be related within each cluster. That works better when the clusters don’t overlap. [http://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld]
-Unlike [[supervised]] learning, unsupervised learning is used with data sets without historical data. An unsupervised learning algorithm explores surpassed data to find the structure. This kind of learning works best for transactional data; for instance, it helps in identifying customer segments and clusters with certain attributes—this is often used in content personalization. Popular techniques where unsupervised learning is used also include self-organizing maps, nearest neighbor mappig, singular value decomposition, and [[K-Means]] clustering. Basically, online recommendations, identification of data outliers, and segment text topics are all examples of unsupervised learning. [http://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn]
+Unlike [[supervised]] learning, unsupervised learning is used with data sets without historical data. An unsupervised learning algorithm explores surpassed data to find the structure. This kind of learning works best for transactional data; for instance, it helps in identifying customer segments and clusters with certain attributes—this is often used in content personalization. Popular techniques where unsupervised learning is used also include self-organizing maps, nearest neighbor mappig, singular value decomposition, and [[K-Means]] [[clustering]]. Basically, online recommendations, identification of data outliers, and segment text topics are all examples of unsupervised learning. [http://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn]
 http://s3.amazonaws.com/static2.simplilearn.com/ice9/free_resources_article_thumb/Machine_Learning_3.jpg