Python Basics for ML | Numbers, Operators, Variables, Keywords, Strings

This is Lecture 3 of the Python for ML course. In this video, we move from theory to hands-on coding and build a strong foundation in Python basics that you will use everywhere in AI/ML.

What we cover in this lecture:
Recap of second lecture (Numbers, Variables, Keywords)
Python print() function in depth (objects, sep, end, multiple arguments)
Case sensitivity in Python (why print ≠ Print)
Comments in Python – single-line and multi-line, why they are important in real projects
Escape sequences: \n, \t, \\, quotes inside strings and how to fix common syntax errors
Numbers in Python: integers, floats, complex numbers
Arithmetic operators: +, -, *, /, //, %, **
Quotient, remainder, floor division, modulus with positive and negative numbers
Built-in numeric functions: type(), int(), float(), complex(), abs(), round(), pow()
Variables and identifiers: what they are, how they are stored in memory, id() function
Variable naming rules and best practices
Dynamic typing in Python vs static typing in C++/Java
Basic data types: int, float, complex, string, bool, None, and a brief intro to list/tuple/dict
Operators in Python- Arithmetic, Logical, Assignment and Membership operators.

By the end of this lecture, you will understand how Python handles numbers, variables, and strings internally and how to write clean, readable beginner-friendly code that is ready for further AI/ML work.

If you are planning to become a Machine Learning Engineer, Data Scientist, or AI Researcher, these basics are absolutely essential.

👉 Make sure to:
Pause and make your own notes
Try to run the Python code yourself

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