AI Study Plan
Choose a path that follows data and evaluation before deployment.
How it works
Start with the foundations and mathematics, then learn the data contract and splitting rules. Train simple baselines before neural models. Use the JavaScript labs to understand actual tensor and rendering APIs, and the reliability unit to separate generation from permissions. Finish with a project report that names the dataset, split, baseline, metric and limitations.
Run the code
Try in playground loads this complete standard-library Python example without executing it. Choose Run when ready. Local review uses Python 3.12.14; the existing browser editor uses Pyodide 0.27.7. These small fictional examples need no account, external data, model-provider key or database service.
Example
steps = ["Define task and row","Validate and split data","Fit a baseline","Train and compare","Check held-out errors","Document limits and review policy"]
for number,step in enumerate(steps,1):
print(f"{number}. {step}")
Expected result
1. Define task and row 2. Validate and split data 3. Fit a baseline 4. Train and compare 5. Check held-out errors 6. Document limits and review policy
The plan is based on outcomes rather than a promise to become an expert in a fixed number of days. Keep a record of refusals and failed ideas as well as successful fits. The course offers no certificate, job guarantee or deployed model service.