Machine Learning in Telecom: From Basics to Real-World Use Cases

Machine Learning in Telecom: From Basics to Real-World Use Cases

Master the complete application of Machine Learning in telecom. Build expertise in supervised, unsupervised, and reinforcement learning to predict trends, optimize networks, and detect issues.

Learning Path

9 Courses

4 beginner

2 intermediate

3 advanced

Rs. 3000

Rs. 5000

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This comprehensive course provides a deep dive into the entire spectrum of machine learning and its transformative role in the telecommunications industry. You will build a strong foundation by exploring all key ML types—Supervised, Unsupervised, and Reinforcement Learning—and their specific use cases, such as predicting user throughput and optimizing dynamic networks. The curriculum offers in-depth technical modules on essential algorithms including Linear and Logistic Regression, Decision Trees, Random Forests, and K-Means Clustering, all applied to real telecom data. You will learn not just the "how" but the "why," covering crucial concepts like cost functions, gradient descent, and managing overfitting. The course culminates in a hands-on capstone project where you will execute the full ML workflow to build a predictive analytics model for a network scenario. You will finish with the end-to-end skills to design, build, and evaluate intelligent systems that solve critical challenges in modern telecom.

9 Courses