Some Machine Learning Algorithms And Processes

[Japo_Japo]

If you’re studying what is Machine Learning, you should familiarize yourself with standard Machine Learning algorithms and processes. These include neural networks, decision trees, random forests, associations, and sequence discovery, gradient boosting and bagging, support vector machines, self-organizing maps, k-means clustering, Bayesian networks, Gaussian mixture models, and more.

There are other machine learning tools and processes that leverage various algorithms to get the most value out of big data. These include:

• Comprehensive data quality and management
• GUIs for building models and process flows
• Interactive data exploration and visualization of model results
• Comparisons of different Machine Learning models to quickly identify the best one
• Automated ensemble model evaluation to determine the best performers
• Easy model deployment so you can get repeatable, reliable results quickly
• An integrated end-to-end platform for the automation of the data-to-decision process

[Raghav1996]

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