#Issues with big data
Machine learning: Programming the system to behave in more customized manner with experience. Machine learning is mostly used in the fields of language processing, forecasting (e.g., stock market trends), pattern recognition, data mining, games, robotics.
Capturing data
Curation
Storage
Searching
Sharing
Transfer
Analysis
Presentation
Machine learning: Programming the system to behave in more customized manner with experience. Machine learning is mostly used in the fields of language processing, forecasting (e.g., stock market trends), pattern recognition, data mining, games, robotics.
Machine learning techniques : recommendation, classification, and clustering (recommendation by search engines, pattern finding, modelling).
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and signal analysis.
Support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
Mining of big data by regular algorithms poses issues, that's when machine learning comes into picture to better reflect trends. Apache Mahout is used for scalable machine learning algorithms. Parallel run can enable handling of such huge data.
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and signal analysis.
Support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
Mining of big data by regular algorithms poses issues, that's when machine learning comes into picture to better reflect trends. Apache Mahout is used for scalable machine learning algorithms. Parallel run can enable handling of such huge data.
Mahout (driver) framework coupled with Hadoop (elephant) infrastructure can solve the issue. MapReduce clustering implementations : k-means, fuzzy k-means, Canopy...
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