Enhance AI-ML Roadmap with detailed learning topics

Domains/AI-ML/Roadmap.md

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 # AI-ML Roadmap
-Beginner → Intermediate → Advanced
+Beginner → Intermediate → Advanced
+
+This roadmap is a suggested plan for learning Artificial Intelligence and Machine Learning skills, including specific topics and areas of interest that can have separate files added to expand on in this folder. 
+
+
+## Beginner: Foundation Skills
+
+### Goal 
+Learn the basics of machine learning and AI, building confidence in the use of Python coding skills, basic data analysis, and visualization skills
+
+### Topics
+- Python Fundamentals: focus on basic Python syntax, variables, loops, functions, NumPy
+- Pandas Library: importing and cleaning data
+- Visualization Libraries: Matplotlib and Seaborn
+- Machine Learning: basics of supervised and unsupervised learning, model basics
+- Model Training: test/train split, linear regression, logistic regression
+- Evaluation: accuracy, precision, recall, F1, confusion matrix
+
+## Intermediate: Applied Machine Learning
+
+### Goal
+Move beyond basic skills into model performance and testing, and engineering skills. 
+
+### Topics
+- Feature Engineering: scaling, domain knowledge
+- Pipelines and Cross-Validation: `Pipeline`, `GridSearchCV`
+- Model Comparison: Random Forest, SVM, KNN
+- Data Leakage and Validation: prevent overfitting and leakage traps
+- Deep Learning: TensorFlow, PyTorch
+- Natural Language Processing (NLP)
+
+## Advanced: Production and Responsible AI
+
+### Goal
+Building competency with deploying, monitoring and scaling machine learning systems, with an understanding of ethics.
+
+### Topics
+- Model Deployment
+- MLOps Concepts: CI/CD, versioning
+- Bias: identify and mitigate bias in models
+- Privacy and Governance: secure data handling