Data & AI · free flashcards
Data and AI flashcards: machine learning, prompt engineering and data analysis
Working with data and models means keeping a lot of vocabulary and judgment in your head: which metric fits, why a model overfits, how to prompt an LLM reliably, how to read an A/B test. These decks turn that into short cards you can review in a few minutes a day.
- Concepts you useOverfitting, precision and recall, embeddings, RAG, joins and group-bys, explained in a line or two.
- Practical examplespandas and SQL snippets, prompt patterns and evaluation metrics you can apply the same day.
- Current, not hypePlain explanations of how LLMs and classic models actually behave, including their limits.
Data & AI decks
Data analysis
Data analysis flashcards: summary statistics, data cleaning, joins and group-by in pandas and SQL, pivot tables, chart choices, A/B tests and causation.
50 cardsMachine learning
Machine learning flashcards: supervised vs unsupervised learning, overfitting, bias-variance, regularization, gradient descent, common models and metrics.
50 cardsPrompt engineering
Prompt engineering flashcards: tokens, context windows, temperature, few-shot and chain-of-thought prompting, structured output, RAG and prompt injection.
50 cardsHow spaced repetition works
- Step 1See a cardRecall the answer before you flip it. Effortful recall is what builds memory.
- Step 2Grade yourselfAgain, Hard, Good or Easy. Each answer sets when the card comes back.
- Step 3Review just in timeCards return right before you would forget them: minutes, then days, then months.
Frequently asked questions
Do I need to know math for the machine learning deck?
A little algebra helps; formulas are typeset with KaTeX and each card explains the idea in words too.
Is prompt engineering still worth learning?
Yes: clear instructions, examples, structured output and retrieval make any model more reliable, and the deck focuses on those fundamentals.
Which tools does the data analysis deck use?
pandas and SQL, plus the statistics and visualization choices behind a sound analysis.
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