Complete Python Course Bootcamp | Python Libraries Unlocked

Implement an if-else program in Python that reads user input and classifies it as a digit or a string using the isdigit method. Practice input handling and conditional flow.

Explore how to implement an if-elif-else ladder in Python, including syntax, flow control, and handling user input to classify numbers as positive, negative, or zero.

build a python program to check divisibility by five or three, print the five message, the buzz message for three or both, and the number otherwise; includes a flowchart.

Explore Python's range function to generate integer sequences, learn start, stop (stop not included), and step parameters, and see how unpacking and loop usage control output.

Create a 3x3 square pattern with nested for loops and range. Print stars on the same line using the end parameter, then insert a blank line after each row.

Learn to build a pyramid star pattern with nested while loops, calculating stars and left blanks per row using a simple formula and printing approach.

Explore the break keyword in Python by building a program with a loop that reads numbers until an alphabet breaks the loop, then computes the average using sum and total_length.

Master slicing in Python to extract list portions using start, end, and optional step; leverage positive and negative indexing and colon syntax to include end items and reverse order.

Master loops and lists by iterating list items with a for loop, printing index and item in tabular form, and using enumerate to simplify indexing.

Count how many times each number appears in a list using nested loops and a checked list to avoid duplication, printing counts without list comprehension or filter.

Explore tuples in Python by declaring immutable sequences, accessing items with indexing and slicing, and handling single-item tuples with a trailing comma.

Explore Python set operations with unions, intersections, differences, and symmetric differences on A and B, and implement these concepts in code using built-in methods.

Learn to unpack dictionary data using keys, values, and items, loop with enumerate to create an index‑key‑value table, and extract keys and values in one line.

Learn dictionary comprehension in Python by transforming a salary dictionary: apply a 25% bonus for salaries over 10,000, keeping others unchanged, using item unpacking.

Explore how strings behave in Python, including indexing, slicing, and negative indexing, and learn to reverse strings and count digits using a for loop and membership checks.

Learn how a function is a named, reusable block with a unique name, parameters, a mandatory body, an optional return, and distinguish it from a method inside a class.

Define a Python function with the def keyword, a name, empty parameters, and a colon, then call it to see its print output and built-in function behavior.

Build a basic statistics program in python by importing the statistics module, aliasing as sd, and returning a four-value tuple of median, mean, standard deviation, and mode for a list.

Explore how doc strings document functions, write a docstring with six quotation marks, and use the help function to reveal parameters and returns like mean, median, and standard deviation.

Explore the four types of function arguments in Python—positional, default, keyword, and arbitrary—through practical examples and parameter order concepts.

Explore the difference between args and kwargs, learn how to define functions with unlimited keyword arguments using **kwargs, inspect kwargs as a dictionary, iterate with items(), and print formatted results.

Explore global and local variables in Python, understand namespaces and scope, and learn how non local variables arise in nested functions, with practical examples.

Explore how Python stores objects in namespaces, including built-in, global, and local namespaces, and how these isolated pools carry unique identifiers and lifetimes.

Explore recursion by implementing a recursive function that calls itself, processes user input, and sums numbers, using a base case and short-circuiting to stop.

Explore recursion in Python by building a recursive function that prints numbers from 0 to 9, with a base case to stop at 9 and an optimized condition.

Practice recursion in Python by implementing a factorial calculator using a recursive function, including the base case and recursive calls that multiply up during unwinding.

Explore how iterators in Python convert iterable objects like lists, tuples, and dictionaries with iter(), then use next() to access elements one by one, setting up for generators.

Explore how nested functions work, with an outer function containing an inner function. See how inner functions access outer variables and parameters, enabling encapsulation, helper functions, closures, and decorators.

Explore how decorators add functionality to existing functions by wrapping a simple print with an extra message, and learn two decorator approaches, with and without the @ syntax.

Learn how lambda functions serve as inner functions to create closures and factory functions in Python, demonstrated through computing exponents and returning function objects.

Learn how to use the filter function in python to filter lists and dictionaries by a condition using regular and lambda functions, with examples for even and odd numbers.

Learn to clean a dictionary by filtering out none values and outliers using a lambda-based filter, converting items to a dictionary, and generating a final clean dataset.

Explore object-oriented programming in Python by building a class with name input and age checker methods, using self, type casting to int, and creating and using objects.

Extend the class with max and min methods that compute values from a numbers list. Fix initialization by using a first item to avoid bias and introduce getters and setters.

Explore Python method types—instance, class, and static methods—using decorators, access patterns, and practical examples, plus the property decorator for getters.

Explore python inheritance by comparing an animal base class with a dog derived class, placing common attributes in the parent and specifics in the child.

Explore encapsulation in Python, differentiate public, protected, and private attributes, and implement getters and setters via the property decorator to protect data in a class.

Explore dunder methods and their role in operator overloading and polymorphism, implementing __init__, __str__, __repr__, __abs__, and __add__ to customize object behavior.

Explore how duck typing in Python makes methods matter more than object identity, using dynamic language features and polymorphism to determine behavior.

Learn how Python handles syntax errors, logical errors, and exceptions with try-except-else-finally blocks. Explore common exceptions such as file not found, zero division, and import errors.

Learn to handle JSON in Python by importing the JSON module, converting between dictionaries and JSON strings, and reading from and writing to JSON files with load, dump, and indentation.

Explore the Pillow library in Python to perform image operations, including per-pixel operations, masking, transparency, filters like blurring and smoothing, brightness/contrast/sharpening, adding text, and drawing shapes.

Resize images in pillow by passing a width and height as a tuple, then view the resized result. Use image.size to determine original dimensions and resize to half the original.

Blend two same size images in Pillow using Image.blend with an alpha value between 0 and 1 to control the mix, producing outputs from image one only to image three.

Learn to blur images in Pillow using an image filter. Choose simple blur, box blur, or Gershman blur, display, and save as JPEG.

Explore Pillow image filters, including blur, contour, detail, edge enhancement, emboss, find edges, and smooth and sharpen options to transform images.

Group images in Pillow and crop using left, top, right, and bottom boundaries to adjust the visible area. Flip images with transpose, including horizontal, vertical, and 90-degree rotations.

Learn to draw basic shapes on images using Pillow, including lines, arcs and semicircles, and create ellipses, rectangles, and rounded rectangles with position, size, color, and fill options.

Learn how to convert colors from text to rgb values using image color utilities, including hex codes and color names, and display the results in code.

Discover how numpy provides fast numerical operations with array-based data in Python, covering single and multi-dimensional arrays, indexing and subscripts, and performance advantages over Python lists.

Install numpy via pip in the terminal, import numpy as np, create arrays from Python lists with np.array, print them, and access elements by 0-based indexing.

Explore dimensions and shapes in numpy arrays, from single to multi-dimensional forms. Learn how shape and dimension differ, read memory diagrams, and map axes in two- and three-dimensional arrays.

Explore practical work on dimensions and shapes in Python arrays, learning how to determine and print the dimension and shape of one-, two-, and three-dimensional data.

Explore numpy arange to create arrays with start, end, and step, learn reshaping to multi-dimensional arrays, and generate arrays of ones and zeros for versatile data handling.

Learn how numpy's flatten converts multi-dimensional arrays to a single dimension, using C order and F order (column-major), and how transpose swaps rows and columns.

Learn how numpy stacking works, including stick versus concatenation, how stacking merges arrays while keeping them separate, and the depth stack concept (destek) with practical examples.

Explore the addition function of the collector module and the center function of the character module in NumPy, with hands-on examples that merge lists by position and center strings.

Explore how the capitalized, title, lower, and upper functions format strings in Python, with practical examples like hello and Python.

Master stripping in numpy by removing whitespace or chosen characters from strings, using strip on single and nested arrays with step-by-step looping examples.

Explore NumPy arithmetic on a 3x3 array and a 1d vector, performing addition, subtraction, multiplication, and division, and compare row-wise versus column-wise results.

Learn the basics of slicing in numpy, from single-dimensional arrays to multi-dimensional arrays, using start, end (end exclusive), and step parameters on zero-based indices to select rows and columns.

Learn how to iterate through multi-dimensional numpy arrays efficiently using a built-in iterating function, avoiding complex nested loops.

Explore numpy resize, which changes an array to a new shape without honoring the original element count, unlike reshape; elements may repeat to fill the new size.

Explore internal numpy operations by adding arrays element-wise, performing row-wise and column-wise sums, and understanding how numbers combine across two dimensions.