AIML 2 track Learning Path & curriculum.xlsx

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2	Packaging Model	Structure	Track	Level	Focus Areas	Tooling & Project Tags	Learning Intent
3	2-Track Learning Path	AI + ML Full Pathway	Track 1 = AI	Beginner	Python Fundamentals, NumPy, Pandas, Data Viz	Python, Jupyter, Matplotlib	Build foundational fluency in data manipulation and visualization
4				INTERMEDIATE	Supervised Learning, Pipelines, Feature Engineering	Scikit-learn, Pandas, MLflow	Apply AI methods in realistic workflows with model selection and interpretation
5				ADVANCED	Neural Networks, NLP, MLOps, Full-Stack AI App	TensorFlow, Hugging Face, Docker, FastAPI	Design, deploy, and explain real-world AI solutions end-to-end
6			Track 2 = ML	BEGINNER	Same foundational modules as AI Beginner: Python, NumPy, Pandas, Data Visualization	Python, Pandas, Seaborn	Equip learners with essential pre-ML skills without redundant rework
7				INTERMEDIATE	Regression, Clustering, Dimensionality Reduction, Real-world Data Application	Scikit-learn, Real-world datasets	Build algorithmic understanding, data handling, and model evaluation strategies
8				ADVANCED	ML Pipelines, Hyperparameter Tuning, Model Interpretability, Deployment	Sklearn Pipelines, Optuna, XGBoost, Docker	Enable production-ready ML skills: tuning, pipeline building, explainability, and deployment
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