Full Stack AI and Data Science Professional

What you'll learn

• Frame business problems and translate them into measurable ML objectives
• Write production-ready Python and use libraries for data and model development
• Apply linear algebra, calculus, and probability fundamentals to ML models
• Design ETL pipelines, clean data, and build data warehouses with SQL
• Perform exploratory data analysis and create actionable visualizations
• Train and evaluate supervised and unsupervised learning models
• Build, tune, and deploy neural networks for vision, tabular, and sequence data
• Fine-tune and evaluate NLP models and large language models for real tasks
• Implement MLOps practices: CI/CD, monitoring, versioning, and governance
• Design cost-aware cloud pipelines and scalable data engineering solutions

Prerequisites

Basic Python and programming familiarity, comfort with high-school math and introductory statistics; SQL knowledge recommended but not required.

Skills you'll gain

• Problem framing and experimental design
• Python for data and ML
• Data engineering and ETL
• SQL and data warehousing
• Exploratory data analysis and visualization
• Supervised and unsupervised modeling
• Deep learning and neural networks
• NLP and large language models
• MLOps, deployment, and monitoring
• Cloud-based scalable pipelines
• Reproducibility, governance, and security