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.
9 Courses
4 beginner
2 intermediate
3 advanced
Rs. 3000
Rs. 5000
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Certificate on completion
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.
This course explores how machine learning predicts trends and optimizes telecom networks. You will learn to apply supervised, unsupervised, and reinforcement learning to solve key industry challenges.
This course explores how machine learning predicts trends and optimizes telecom networks. You will learn to apply key ML types to solve challenges like maximizing user throughput, network efficiency.
Master the core techniques of supervised learning by applying regression for precise signal prediction, classification for automated network issue detection.
Harness logistic regression to automatically classify network states and security threats. Master the sigmoid function and decision boundaries.
Go beyond the basics and master the mathematical engine of linear regression for telecom. Learn to build, evaluate, optimize predictive models for network performance.
Uncover hidden patterns in your unlabeled network data using unsupervised learning. Apply clustering and anomaly detection to autonomously segment users.
Master reinforcement learning to build self-optimizing networks that learn from experience. Train AI agents to make autonomous decisions that maximize network efficiency and performance.
Master Decision Trees and Random Forests to build powerful, interpretable models for telecom. Learn to reduce variance, rank feature importance, leverage ensemble learning.
Master the complete machine learning workflow from raw data to deployed model. Apply each step to a hands-on capstone project in network predictive analytics.