Master the foundations of AI and ML, from supervised learning to neural networks. This course blends theory with hands-on projects using Python, making it ideal for aspiring AI engineers.
Brief Content:
Introduction to AI & ML concepts
Supervised, Unsupervised & Reinforcement Learning
Model evaluation and deployment
Tools: Python, Scikit-learn, Jupyter Notebooks
Mapped Job Roles:
AI/ML Engineer, Data Analyst, AI Research Assistant, AI Product Developer
Learning Outcomes:
Understand key AI/ML algorithms
Build and evaluate machine learning models
Apply ML techniques to real-world problems
