Practical guides to learning AI and machine learning, written by Gaurav Jain. Worked examples, projects with complete code and learning plans you can follow.
Permutation feature importance is how far a model's score drops when one feature is shuffled. Python code, a comparison with MDI and grouped shuffles.
tutorial8 min read
Test a malicious retrieved instruction with deterministic proposal fixtures and enforce a read-only application capability boundary.
tutorial4 min read
Reproduce a shared-default bug, repair it with a sentinel, and distinguish a fresh collection from intentional accumulation.
troubleshooting4 min read
Follow a nested dictionary through assignment, shallow copying and deep copying, then choose the right copying boundary for a data workflow.
troubleshooting4 min read
Create an isolated Python environment with uv, bind package operations to that interpreter and verify isolation before diagnosing import failures.
tutorial4 min read
model.eval() switches dropout and batch norm to inference behavior; torch.no_grad() stops gradient tracking. Use both to validate, with checked code.
troubleshooting7 min read
Retrieve from original public teaching notes, generate with a pinned local model and publish only a source-matching quotation or an explicit abstention.
project5 min read
Build an offline evidence-retrieval test set, compare rankings with explicit relevance judgments and separate retrieval success from answer correctness.
tutorial4 min read
Stream a CSV with DictReader, validate malformed records and calculate an exact total while keeping only the current record and running counters.
tutorial4 min read
The courses take each idea from the intuition and the maths to code you write yourself, from your first line of Python to machine learning.