From Python Basics to AI: Learn to Code & Build AI

Explore variables and data types in Python, including strings, numbers, booleans, casting, and input/output. Learn valid naming conventions, quotes usage, and rules for illegal names.

Explore Python data types, including strings, numeric types (int, float, complex), sequences (list, tuple, range), dictionary, sets (set, frozen), booleans, bytes, and none, with practical examples.

Explore Python's numeric types—integers, floats, and complex numbers—and learn type conversions between them, plus generating a random integer in a defined range.

Learn Python strings through quotes handling, indexing and slicing, membership tests with in and not in, length checks, and modifications such as upper, lower, strip, replace, split, and concatenation.

Learn Python operators—from arithmetic like plus and exponent to assignment, comparison, and logical operators, then explore bitwise operators and practice with examples using is, or, not, and left shift.

Explore Python lists for data science, including zero indexing, negative indexing, duplicates, and multiple data types, and perform append, extend, insert, remove, pop, sort, reverse, and joining lists.

Explore sets in Python as unordered, changeable collections that store multiple items in a single variable. They lack an index and are not subscribable, and have no duplicates.

Understand Python dictionaries as order and changeable key-value stores with no duplicates, and learn to create, copy, update, remove items, and access nested dictionaries.

Learn Python control flow with if, else, and elif, understand indentation, compare values with operators, and use nested and else branches to handle outcomes.

Learn how to use Python while loops, including break and continue, and how else clauses affect loop execution. See iterations from 1 to 5 with practical flow control.

Explore Python functions by defining with def, passing parameters and arguments, returning values, and calling functions. Learn about default and arbitrary arguments, keyword arguments, for loops, and recursion.

Explore Python lambda functions, small anonymous functions that take any number of arguments but return a single expression, with examples of using them inside other functions and simple arithmetic.

This lecture covers Python classes as blueprints for objects, using init to set name, age, and gender, and string function to display data and other methods to modify it.

Explore Python inheritance by defining a parent class and a child class, using super to inherit methods and properties, and building person and student examples.

Explore Python polymorphism by modeling vehicles, such as cars, boats, and planes, with a shared move method across classes, using inheritance to reduce duplication and emphasize object-oriented design.

Explore python scopes to distinguish local scope from global scope, how local and global variables are accessed by functions, and the non-local keyword for outer function binding.

Learn to use the JSON module in Python for data management by converting Python objects with dumps, loading with loads, and building nested data structures such as dictionaries and lists.

Learn how to handle files in Python using the open function, with modes for reading, writing, appending, and creating new files, plus binary and text options and basic delete operations.

Explore the artificial intelligence, machine learning, and deep learning hierarchy with definitions and real-world examples like chatbots, recommendations, and image recognition. Choose ai, ml, or dl by data and complexity.