Course Modules

Module 1: Foundations of Machine Learning for Telecommunications

• Introduction to Machine Learning and Artificial Intelligence
• Machine learning lifecycle
• Supervised, unsupervised, and reinforcement learning
• Telecom applications of machine learning
• Challenges in ML adoption within telecom organizations
• Data requirements for machine learning models
• Case Study & Practical Application: Identifying suitable machine learning applications for a mobile network operator.

Module 2: Telecom Data Preparation and Feature Engineering

• Telecom data sources and structures
• Data cleaning and transformation
• Feature selection and engineering
• Handling large-scale telecom datasets
• Preparing data for predictive modelling
• Data quality challenges
• Case Study & Practical Application: Preparing subscriber and network datasets for machine learning analysis.

Module 3: Predictive Analytics for Network Performance Optimization

• Network performance prediction
• Traffic forecasting
• Capacity optimization
• Predictive maintenance models
• Quality of Service (QoS) prediction
• Resource optimization
• Case Study & Practical Application: Building a predictive model for identifying potential network performance degradation.

Module 4: Machine Learning for Customer Intelligence and Security

• Customer segmentation models
• Churn prediction
• Fraud detection analytics
• Subscriber behaviour analysis
• Recommendation systems
• Cybersecurity threat detection using ML
• Case Study & Practical Application: Developing a machine learning model to predict customer churn and improve retention strategies.

Module 5: Machine Learning Deployment and Future Applications

• Machine learning operationalization (MLOps)
• Model monitoring and improvement
• Cloud and edge-based ML deployment
• Explainable AI principles
• Future ML applications in 5G and 6G networks
• Case Study & Practical Application: Developing a machine learning implementation roadmap for a telecommunications organization.