AI Generalist Self-Evaluation

This brief self-evaluation helps you assess your AI knowledge and skills across core areas. For each statement, select the answer that best matches your confidence. Your results will include personalized learning resources for any gaps!

1. Fundamentals: I can explain in my own words what AI, machine learning, and deep learning are, and how they relate to one another.

Yes Somewhat No / Not sure

2. Math & Statistics: I understand why vectors, matrices, and probability underpin ML algorithms.

Yes Somewhat No / Not sure

3. Algorithms: I know at least three common ML families (decision trees, neural networks, clustering) and can pick the right one for a given problem.

Yes Somewhat No / Not sure

4. Data Workflow: I’m confident outlining an end-to-end pipeline: data cleaning, feature engineering, train/test splitting, and EDA.

Yes Somewhat No / Not sure

5. Model Evaluation: I can choose and interpret metrics (accuracy, F1, RMSE, etc.) and know when to favor precision/recall versus overall accuracy.

Yes Somewhat No / Not sure

6. Deep Learning & LLMs: I’ve used a neural-network framework (TensorFlow or PyTorch) or can explain how transformers work.

Yes Somewhat No / Not sure

7. Computer Vision: I understand at a high level how convolutional neural networks process images and why they excel at vision tasks.

Yes Somewhat No / Not sure

8. MLOps & Deployment: I know the key steps to containerize a model, deploy it as an API, and set up basic monitoring.

Yes Somewhat No / Not sure

9. Ethics & Governance: I’m aware of common AI fairness and bias pitfalls, and general strategies to mitigate them.

Yes Somewhat No / Not sure

10. Product Integration: I can describe how to frame an AI feature end-to-end—define the problem, identify data needs, outputs, and success metrics.

Yes Somewhat No / Not sure

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