Complete Python 3 Bootcamp course: Zero to Hero 2023

Set up your Python environment with the Anaconda distribution, install it on Windows, Mac, or Linux, and launch Jupiter notebook to run and organize course notebooks.

Access the complete Python notebook reference repository on YouTube by searching Aderet Valley, and explore 14 sections with exercises and solutions, plus coding challenges starting with object and data structure.

Explore Python 3 numbers: integers, floats, and complex numbers, including complex conjugates, rounding behavior, floating point pitfalls, and precise alternatives via the decimal module, plus binary, octal, and hex conversions.

Master Python strings by learning indexing and slicing from zero, concatenation, repetition, and common methods like upper, lower, count, and split.

Explore the list data structure in Python, including creating mixed-type lists, indexing and slicing, finding length with len, appending and popping, sorting with reverse, nesting lists, and list comprehension.

Explore the dictionary data structure in Python to map keys to values, access nested dictionaries, and work with multiple value types in practical examples.

Explore the tuple data structure, its immutability, and common operations like indexing and count, highlighting when to use tuples over lists for data integrity.

Explore sets as unordered collections of unique elements, learn to create and add items, and typecast lists to sets, while defining booleans as true or false.

Open, read, and write files in Python with the built-in open function and file objects, using modes, seek to reset, and readlines to capture lines.

Practice the object data structure with coding questions by running solutions in empty cells, and consult the solutions notebook for all data structures learned in the course materials.

Learn how to use Python comparison operators—greater than, less than, equal to, not equal to, and their inclusive forms—to produce boolean true or false for integer comparisons.

Explore chained comparison operators in Python, using and and or to build multi-condition tests, with short-circuit truth evaluation and practical boolean examples.

Learn how boolean values and comparison operators drive decision making in Python using if, elif, and else. Explore short-circuit logic, multiple conditions, and boolean conversions.

Explore Python's if else conditional statements, using true conditions to run code, understand indentation, and apply multiple conditions, input handling, type casting, and modulo for even, odd, or divisible cases.

Explore loops in Python, using while and for loops, break and continue, and understanding iterators and iterable objects with range and step concepts.

Use for loops in Python to iterate over strings, lists, tuples, and dictionaries, printing items. Use modulo and conditionals to select even numbers and sum lists and dictionaries with items().

Master while loops in Python, using while condition is true and indentation to repeat code until the condition is false. Learn break, continue, and pass to control flow.

Master Python iteration and operators: use range for loops, enumerate with tuple unpacking, and zip for parallel lists; then apply in checks, min, max, and random numbers.

Explore Python list comprehension to build lists in one line, from iterating over strings to squaring numbers and filtering even values. Apply nested comprehensions to power numbers and convert temperatures.

Build a list of integers from one to ten using a loop, and a second list with list comprehension and a conditional operator; check the notebook and repository for solutions.

Learn how functions act as machines that take input, process it with code, and return output; define multiple inputs, reuse code, and call functions repeatedly in Python.

Learn how to define and use functions in Python with def, parameters, and indentation; explore printing, returning sums, and checking even, odd, and prime numbers, including square-root optimization with math.

Learn how to use lambda expressions with map and filter in Python to apply a one-line function, such as squaring numbers, and to filter even values from lists.

Explore how Python uses namespaces and scope to govern variable visibility in nested statements and functions, including global versus local scope and the global keyword.

Explore how multiple functions interact to run code, build a cup-and-ball game, shuffle cups, and validate user input using Python functions.

Learn to write Python functions that compute a square's area from a side length, and return a boolean prime check, with practice using list comprehension and self-testing.

Learn to use try, except, and finally blocks for error handling and exception handling, validating input types and preventing script termination in Python.

Master python error and exception handling through a set of tests that require squaring a user-provided integer and gracefully handling non-integer input.

Learn the basics of object oriented programming in Python, including creating objects with attributes and methods, using self, and defining classes and init methods.

Explore object oriented concepts using Python by creating classes, instances, and attributes, and implementing methods to compute area and perimeter, access types, and manage object state.

Learn inheritance to form derived classes and reuse base class features in Python. See polymorphism with same-named methods and abstract classes that require subclass implementation.

Learn to implement Python's dunder (magic) methods to enable built-in functions on your classes, including initialization, string representation, length, and deletion, demonstrated with a book example.

Create a bank account class with three attributes, including balance and account number. Implement deposit and withdraw methods that update the balance and print 'balance is not available' when insufficient.

This lecture introduces Python decorators, showing how to add functionality to an existing function by wrapping it with a decorator, and how to toggle it to reduce complexity.

Learn how Python generators use yield to produce values on demand, enabling memory-efficient sequences like range and Fibonacci series. Explore iterators, next(), and the difference between iterables and iterators.