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.
Learn the basics of machine learning and AI, building confidence in the use of Python coding skills, basic data analysis, and visualization skills
- 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
Move beyond basic skills into model performance and testing, and engineering skills.
- 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)
Building competency with deploying, monitoring and scaling machine learning systems, with an understanding of ethics.
- Model Deployment
- MLOps Concepts: CI/CD, versioning
- Bias: identify and mitigate bias in models
- Privacy and Governance: secure data handling