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It constructs multiple decisiontrees and combines their predictions to achieve accurate results in identifying different types of network traffic SupportVectorMachines (SVM) : SVM is used for both classification and anomaly detection.
Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and dataengineers, and determining appropriate key performance indicator (KPI) metrics. Deep learning algorithms are neural networks modeled after the human brain.
Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decisiontrees, supportvectormachines, and neural networks gained popularity.
Scala is worth knowing if youre looking to branch into dataengineering and working with big data more as its helpful for scaling applications. Scikit-learn also earns a top spot thanks to its success with predictive analytics and general machine learning.
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