Course Details

Courses Descriptions

MODULE OBJECTIVE

What you'll achieve

Gain fluency in Python syntax, all major data structures, control flow, and functional programming patterns — the essential toolkit for every data science and AI workflow.

SECTION 01 — Python Fundamentals

Language basics, operators, and I/O

• What is a programming language? Why Python for AI?
• Variables, types: int, float, str, bool, complex
• Operators: arithmetic, comparison, logical, bitwise, assignment
• Input/output, type conversion, f-strings

SECTION 02 — Data Structures

Lists, tuples, sets, dicts, strings

• Lists: indexing, slicing, mutability, methods
• Tuples: immutability and packing/unpacking
• Sets: uniqueness, union, intersection, difference
• Dictionaries: key-value pairs, nesting, iteration
• String methods: split, join, format, f-strings

SECTION 03 — Control Flow

Decision-making and iteration

• if / elif / else conditional statements
• for loops: range
• while loops: condition-based repetition
• break, continue, pass, nested loops

SECTION 04 — Functions & Functional Tools

Reusable, expressive Python code

• def, parameters, return, default args
• Lambda: anonymous one-liners
• enumerate() and zip()
• List comprehensions
• map(), filter(), reduce()

SECTION 05 — Classes & OOP & Practical Task: Projects

Object-oriented programming in Python

• Defining classes: init, self, instance variables
• Methods, properties, dunder methods
• Inheritance and method overriding
• OOP patterns in ML frameworks (sklearn, PyTorch)

Build Small Library, Web Scraper, Calculators, Password generator, Todo List App, Number Guesser

KEY TAKEAWAYS

1. Master lists, dicts, sets — 80% of data wrangling uses them

2. List comprehensions and lambdas make code Pythonic

3. Functions are first-class objects — pass, return, store them

4. OOP underpins every major ML/DL framework