This course provides a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML) with a focus on applications in the telecommunications industry.

Learners will begin with an overview of AI/ML concepts, followed by a deep dive into essential statistical foundations and Python programming for data analysis. The course covers key machine learning techniques, including supervised and unsupervised learning, model evaluation, and optimization methods.

Finally, real-world use cases in telecom, such as network optimization, fraud detection, and customer experience enhancement, will be explored.

By the end of the course, participants will have a strong foundation in AI/ML and its practical implementations.

Course includes -

AI Basics

Machine Learning Overview

Types of Machine Learning

Deep Learning

Applications in Telecom

Introduction to Statistics ·Overview of Python & its libraries ·Descriptive Statistics

Central Tendency, Dispersion & Visualization (hands on – excel & python)

Probability and Distributions

Normal, Binomial & Poisson Distribution (hands on – excel & python)

Inferential Statistics

Hypothesis testing (t-tests)

Confidence Interval

Introduction to Supervised Learning

Linear Regression

Hypothesis, Cost function, Gradient Descent, Regularization

Example of telecom network

Logistic Regression

Sigmoid Function, Decision Boundary, Anomaly detection

Example of telecom network

Throughout the course, participants will engage in hands-on projects and case studies, applying AI/ML techniques to real telecom datasets. By the end of the program, learners will have a strong technical foundation in AI/ML, practical coding skills, and the ability to implement AI-driven solutions tailored to the telecommunications sector.