AI, Machine Learning, Statistics & Python

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 BasicsMachine Learning OverviewTypes of Machine LearningDeep LearningApplications in TelecomIntroduction to Statistics ·Overview of Python & its libraries ·Descriptive StatisticsCentral Tendency, Dispersion & Visualization (hands on - excel & python)Probability and DistributionsNormal, Binomial & Poisson Distribution (hands on - excel & python)Inferential StatisticsHypothesis testing (t-tests)Confidence IntervalIntroduction to Supervised LearningLinear RegressionHypothesis, Cost function, Gradient Descent, RegularizationExample of telecom networkLogistic RegressionSigmoid Function, Decision Boundary, Anomaly detectionExample of telecom networkThroughout 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.