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.

Learn how to assign multiple values to variables and unpack collections into x, y, z. Understand printing methods and using the global keyword to access global variables.

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 string formatting with f-strings, placeholders, and modifiers, including two-decimal formats and escape characters. Explore essential string methods like capitalize, upper, and lower for text handling.

Learn how Python evaluates expressions to boolean true or false, understand the bool type, and distinguish truthy and falsy values, including nonempty items versus zero, empty strings, and empty containers.

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 how Python tuples store multiple items in an ordered, immutable collection with round brackets, featuring indexing, unpacking, and converting to lists for modification.

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.

Learn how Python for loops iterate over sequences like lists, tuples, dictionaries, sets, and strings. Explore controlling loop flow with break, continue, else, and range, plus nested loops.

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.

Learn how Python arrays store multiple values in a single variable, access elements by index, measure length, loop through items, and add or remove elements with simple methods.

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.

Learn how Python iterators work by using the iterator protocol with iter and next, looping over iterables like strings and custom classes, and safely handling stop iteration.

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.

Explore Python modules as code libraries—files containing reusable functions, imported to be used in your applications—through examples in Jupyter notebooks, with Pandas, NumPy, and Matplotlib for data analysis and visualization.

Explore using the date time module in Python to work with date objects, access year, month, day, hour, minute, second, and microsecond, and print formatted dates or create custom dates.

Discover how Python's math module, imported with import math, provides built-in functions for min, max, abs, power, floor, pi, and logarithms.

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.

Master Python regex to search, match, find all, split, and substitute text using the re module. Apply regex for whitespace handling and basic text cleaning to streamline coding tasks.

Learn how to use pip, the Python package manager, to install libraries and modules like camel case, enabling you to manage dependencies in Colab or on your computer.

Learn to handle errors in Python with try and except, and use else and finally blocks to run code when no error occurs and to handle exceptions.

Master Python user input and simple interaction by prompting for a name and printing a response. Learn the difference between input in Python 3 and raw_input in Python 2.7.

Explore Python string formatting with f-strings, including formatting prices, taxes, and totals. Apply conditional expressions to label prices as expensive or cheap and customize outputs.

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.

Master AI fundamentals and advanced techniques from Python to NLP and computer vision, using TensorFlow, PyTorch, and OpenCV, with a capstone project to deploy real-world AI solutions.

Understand what artificial intelligence is and how machines simulate human thinking, learning, and decision making, including milestones like Deep Blue and AlphaGo.

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.

Explore the core components of AI—natural language processing, computer vision, expert systems, robotics, and knowledge representation—and see how they enable chatbots, autonomous vehicles, and semantic search.