Introduction
Introduction
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1. Machine Learning (ML) and Its Types
Predicting telecom network trends with ML
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ML types: Supervised, unsupervised, and reinforcement
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Supervised learning: Learning from labeled data
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Unsupervised learning: Discovering patterns in telecom data
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Reinforcement learning: Optimizing dynamic networks
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2. ML Use Cases in Telecom Networks
How telecom networks use ML to optimize user throughput
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Benefits of ML in telecom
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3. Supervised Learning in Telecom
Supervised learning: Regression vs. classification
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Signal prediction: Understanding regression in telecom
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Network issue detection: Classification in action
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4. Linear Regression in Telecom
Linear regression basics for telecom analytics
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Using hypothesis testing to predict network performance
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Cost function explained: Measuring telecom model accuracy
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Gradient descent: Fine-tuning network models
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Overfitting vs. underfitting: Optimizing for telecom predictions
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5. Logistic Regression in Telecom
Classifying network issues with logistic regression
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Sigmoid function: Converting data into decisions
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Understanding the logistic hypothesis for telecom predictions
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Decision boundaries: Separating normal and malicious traffic
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Cost function in logistic regression
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6. Unsupervised Learning in Telecom
What is unsupervised learning? Finding patterns without labels
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Self-organizing networks: The power of clustering
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K-means clustering: Segmenting subscribers
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Gaussian distribution: Understanding telecom data spread
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Anomaly detection: Spotting outliers in network performance
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7. Reinforcement Learning in Telecom
Reinforcement learning basics
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How reinforcement learning works
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8. Decision Tree and Random Forest
Decision tree
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Variance reduction and feature importance
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Random forest and how it works
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Why use random forest?
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Random forest vs. decision tree
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9. Practical ML Application: Telecom
ML workflow
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Data collection and preparation
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ML project: Network predictive analytics
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Conclusion
Final thoughts
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