Python for AI

The last unit of the course, and the one you learn with your hands on a keyboard rather than by reading.

1Where this unit is examined

Unit 7 carries no theory marks

In the syllabus the heading reads “UNIT 7: ADVANCE PYTHON (To be assessed through Practicals)”. It is given 10 practical hours, no theory hours and no marks in the written paper — the same shape as Unit 4, Statistical Data.

So this page is a map, not a course. It tells you what the pieces are and where to go and actually learn them.

2Jupyter Notebook

What it is

Jupyter Notebook is where AI programs are written and run — one cell at a time. You type a few lines, run just those lines, and see the result immediately underneath before writing the next cell.

That matters for AI work specifically: loading a dataset can take a while, and in a notebook you load it once and then experiment on it cell by cell, instead of re-running the whole program every time you change something.

3The three libraries

Library	What it is for
NumPy	Fast arrays of numbers, and the arithmetic that runs on whole arrays at once
Pandas	Tables — reading a CSV, selecting rows and columns, handling missing values
Matplotlib	Charts — turning a table into a picture, which is data exploration

You have met all three already

Every one of those does something this course has already described in words. NumPy is the numbers, Pandas is the structured data of Unit 4, and Matplotlib is data exploration, stage 3 of the AI Project Cycle.

4Where to actually learn this

This site already teaches Python properly, from the first print() onward, with a real Python running in the page so you can change an example and re-run it. Reprinting a shorter, worse version of that here would help nobody.

Start here

The Computer Science Class 11 track covers variables, data types, operators, conditionals, loops, strings, lists, tuples, dictionaries and modules — everything Unit 7’s “Python recap” asks for, and a good deal more.

Start with Python from the very beginning, and when you want the three libraries themselves, the Informatics Practices track covers NumPy and Pandas in full.

For your practical file

The syllabus asks for a practical file with a minimum of 15 programs. Write them as you go through the Python track rather than all at once at the end — a program you wrote to answer a question you actually had is one you can explain in the viva.

5Check yourself

Quick Check

How is Unit 7, Advance Python, assessed?

Quick Check

What is Jupyter Notebook used for?

Quick Check

Which library would you use to read a CSV file into a table?

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