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AI & ML Use Cases in Telecom with Practical Applications

Name: AI & ML Use Cases in Telecom with Practical Applications
Price: 4999 INR
Availability: InStock

by Rajesh Kumar · in General · Last updated: August 8, 2026

Course Description

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the telecom industry by enabling intelligent network optimization, predictive maintenance and automated decision-making. This course explores key AI & ML use cases in telecom, including anomaly detection, traffic prediction, customer experience enhancement and 5G network optimization. Designed for telecom engineers, data professionals and technology leaders, the program focuses on practical applications and real-world industry scenarios.

Internal Link:

https://edunavo.com/courses/

Learners can explore our complete range of Telecom Training Courses designed for students and professionals.

https://edunavo.com/courses/5g-l2l3-mastery-course/

https://edunavo.com/labs/

Practical exposure is provided through Hands-On Telecom Labs that simulate real network and optimization scenarios.

External Link

https://www.3gpp.org

Internal Link

https://edunavo.com/courses/introduction-to-machine-learning/

 

 

 

 

[migrated-from-learnpress]

Curriculum

6 Sections • 44 Lessons • 25 hour

1.11.1: Introduction: Why AI/ML matters in 4G/5G networks - Basics

1.21.2:Typical telecom datasets: CDRs, QoS metrics, session logs

1.31.3: Installing Python & Anaconda

1.41.4: Creating and activating a virtual environment

1.51.5: Conda create -n Telecom AI python=3.10

1.61.6:conda activate Telecom AI

1.71.7: Installing key libraries (pandas, numpy, scikit-learn, matplotlib, seaborn, xgboost)

1.81.8:Launching and working with Jupyter Notebooks

1.91.9: Intro to Git & GitHub: initializing repos, committing, pushing code

1.10LAB: Create virtual environment, Run a test notebook, Push your notebook to GitHub

2.12.1: Load dataset: customer billing, usage, complaints

2.22.2: Data Cleaning (Handle nulls, outliers, Feature engineering (tenure_bins, charges_to_float))

2.32.3: Model Building: Logistic Regression, Random Forest

2.42.4: Model Evaluation(Accuracy, F1 Score, ROC Curve)

2.52.5: Visualization: Feature importance & churn distribution

2.62.6: Export results & notebook to GitHub

2.7LAB: End-to-end churn prediction pipeline in Jupyter + GitHub upload.

3.13.1: Load and pre-process device logs (signal strength, temperature, packet drops)

3.23.2: Correlation analysis and feature selection

3.33.3: Model Building: SVM, Gradient Boosting

3.43.4: Hyper parameter tuning with GridSearchCV

3.53.5: Visualization: Fault trends by region/device

3.63.6:Export trained model (.pkl) to GitHub

3.7LAB: Create and test fault prediction notebook with Git version control.

4.14.1: Synthetic dataset: Signal_Strength, Tower_Distance, User_Speed, IsDropped

4.24.2: Data Cleaning: remove anomalies, normalize input features

4.34.3: Modeling: Decision Tree, XGBoost

4.44.4: Evaluation: Confusion matrix, classification report

4.54.5: Visualization: Dropped calls by time/location

4.64.6: Export notebook + visuals to GitHub

4.7LAB: Simulate call drop prediction E2E with visual dashboards.

5.15.1: Data pre-processing: scaling, missing value handling

5.25.2: Model Building: K-Means, Hierarchical Clustering

5.35.3: Dimensionality Reduction: PCA/t-SNE visualization

5.45.4: Visualization: Cluster heatmaps & insights

5.55.5: Export clustering results to GitHub

5.6LAB: Perform customer segmentation and publish notebook to repo.

6.16.1: Data: CDRs, SIM change frequency, call location

6.26.2: Handling class imbalance: SMOTE

6.36.3: Modeling: Isolation Forest, Auto encoders

6.46.4: Evaluation: Precision, Recall, PR Curve

6.56.5: Visualization: Fraud heat map, anomaly score chart

6.66.6: GitHub workflow (Push final code, model, and README.md,Version tracking with commits)

6.7LAB: Build and publish a telecom fraud detection model on GitHub.

Instructor

Rajesh Kumar

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