Applied AI Machine Learning Certificate

Foundations of Python Programming

This course introduces Python programming fundamentals. You'll be equipped with essential skills for data handling and visualization

• Python Fundamentals, Data types, loops, conditions, algorithms and logical operations
• Functions – parameters, calling
• Object Oriented Programming
• Data Handling
• Data Manipulation and Visualization

Machine Learning

This course builds on Python skills to explore machine learning, covering algorithms, evaluation, and introductory deployment techniques, preparing you for real world ML applications.

• Machine Learning Foundations – probability, distributions, bias-variance, overfitting vs underfitting, cross validation techniques
• Supervised Learning – Linear, regression, logistics regression, decision trees, gradient boosting, vector machines
• Evaluation metrics, precision, matrix analysis
• Unsupervised Learning – clustering, dimensionality reduction, anomaly detection
• Deploying and monitoring ML models – serverless, API -based, tools.
• Neural Networks
• Model Productionization – data pipelines, tools, API serving, Monitoring

ML Ops and Cloud Infrastructure

Machine Learning (ML) Ops is an introduction to the processes used to develop, deploy and manage Machine Learning development and operations.

• Define and identify tools for ML Ops
• Versioning and applying model tracking techniques
• ML Infrastructure on the Cloud
• CI/CD (process automation) and integration testing
• Practical implementation

Generative AI

This course explores generative AI, focusing on large language models, prompt engineering, and practical applications like chatbots and image generation, building on prior ML and deployment knowledge:

• Generative AI concepts and applications – ethical considerations
• Large Language Models
• Prompt Engineering
• Generative Applications
• Neural Networks
• Model Productionization