29. What Is Machine Learning (Simple Explanation)
29.5 Practice, Tip and Close
Why. Ideas stick when you classify three real questions from your own week.
What. A short practice that uses only Chapter 29 words — no model training yet.
How.
Chapter practice
- Write three questions from a fictional Mauli Dairy, Pune shop (or your own work).
- Mark each as analytics / ML / generative AI.
- If ML, mark supervised or unsupervised and name the label (or write “no label”).
- List five columns you would need in a clean table for one supervised question.
# Self-check — edit the strings, then run
tasks = [
{"q": "What was last week's average litre sales in Kothrud?", "lane": "analytics"},
{"q": "Will tomorrow's 6 PM order likely be late?", "lane": "ml_supervised", "label": "was_late"},
{"q": "Draft a Diwali offer SMS in Marathi+English", "lane": "gen_ai"},
]
for t in tasks:
print(t)
Practice task
Send yourself a four-line note: one analytics question, one supervised ML question with label name, one unsupervised idea, and one Gen AI idea. Keep it fictional if you lack real data.
Ravindra Bagale's Tip
ML shikaycha excitement theva, pan pehle question clear. Wrong question = beautiful wrong model. Samajla ka?
Thodkyaat sangaycha tar (quick recap)
- ML learns patterns from past data to guess on new cases
- Analytics summarises the past; Gen AI creates new content
- Supervised = label column exists; unsupervised = find structure without labels
- Clean table + clear question before any model
Samajla ka? Aata pudhe jaauya ML workflow — data, features, train, evaluate, predict.