Harness logistic regression to automatically classify network states and security threats. Master the sigmoid function and decision boundaries.
1 Modules
00:18:39
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This course provides a deep dive into using machine learning to build smarter, self-optimizing telecom networks. You will explore the fundamental ML techniques of regression and classification for predicting network behavior and detecting issues. Learn to forecast signal strength to prevent congestion and classify faults for automated resolution. We move beyond theory to practical implementation, focusing on real-world applications that impact key performance indicators. Gain the skills to leverage data for improving network reliability, customer experience, and operational efficiency. Transform raw network data into actionable intelligence and drive the future of autonomous telecommunications.
5.1. Classifying Network Issues with Logistic Regression
00:01:45
5.2. Sigmoid Function Converting Data into Decisions
00:02:59
5.3. Understanding the Logistic Hypothesis for Telecom Predictions
00:05:09
5.4. Decision Boundaries Separating Normal and Malicious Traffic
00:04:42
5.5. Cost Function in Logistic Regression
00:04:04