100 Python Projects + Basics : The Ultimate Python Course

Explore Python lists by creating, indexing, slicing, and modifying data; learn append, insert, extend, and removal methods, plus looping with for, range, and enumerate.

Explore Python sets as unordered, mutable collections of unique items, learn how they remove duplicates, and perform set math—union, intersection, difference, and symmetric difference—while adding, removing, or looping.

Learn how while loops repeat actions until conditions are false in Python, and master break, continue, and else constructs.

Explore Python classes and objects through a car blueprint to learn attributes, methods, self, constructor, and key OOP concepts like inheritance, encapsulation, polymorphism, and magic methods.

Learn how Python's try-except handles errors with a safety net, keeping programs running. Explore four patterns—basic, catch-all, else, and finally—for reliable, user-friendly error handling.

Beginner Python project: shopping list cleaner shows how to clean messy lists by removing duplicates, converting to lowercase, and sorting, using input, split, strip, lower, set, and a list comprehension.

Create a beginner Python file lister that lists all items in a chosen folder, labeling files and folders, using the OS module, user input, and error handling.

Build a beginner-friendly weather checker console app in Python that uses an API key and endpoint to fetch real-time city weather and temperature.

Learn Tkinter, Python's built-in GUI library for desktop apps, and build a main window with widgets such as labels, entry fields, and a greet button that displays a greeting.

Learn how Tkinter frames act as containers to group widgets, organize layouts, and style sections with color, padding, borders, and fixed sizes using config and pack_propagate.

Explore how the Tkinter menu widget creates a menu bar with file and help menus, including new, open, exit, and about, and uses add_command, add_separator, and add_cascade to organize items.

Build practical desktop apps with Python basics through Tkinter projects, from a click counter to an inventory manager, mastering widgets, layouts, files, and user interactions.

Build a Tkinter click counter that updates in real time through event handling, using labels, buttons, frames, and dynamic String Var display, plus keyboard shortcuts for space, backspace, and R.

Build a simple Python calendar app with Tkinter that displays a full month view, supports month and year navigation, and highlights today's date in an offline desktop grid.

Build a desktop inventory manager with Python and Tkinter, enabling add, edit, delete, and search operations. Save and load inventory via csv, with low-stock highlighting for quick insights.

Build web apps with the Flask framework using Python. Learn setup, app structure, Jinja2 templates, static files, forms, and databases through hands-on projects like a task manager.

Explore how to structure a Flask project, from a minimum app with import Flask, create app, define route and start server, to a scalable layout with static and templates folders.

Explore how Flask templates use Jinja2 to render dynamic HTML with placeholders and double curly braces, including variables, expressions, if statements, for loops, includes, and extends base templates.

Learn to work with flask and databases using sqlite, and use flask sqlalchemy to create models, connect to a site db, and perform CRUD operations.

Develop a secure login and registration system with Flask, SQLAlchemy, and WTForms, featuring routing, password hashing, Flask-Login, and templated dashboards.

Build a covid-19 tracker app in Python using Flask, Requests, and Jinja2 to fetch real-time data from disease.sh, display a global summary, country details, and top countries with Bootstrap.

Explore data with pandas by previewing with head and tail, inspecting shape, columns, index, and info, and describing numeric data; then filter, sort, group, and analyze with correlation and aggregate.

Master selecting columns and rows in pandas and filtering data by conditions, including numeric, categorical, and string criteria, with loc, iloc, and SQL-like syntax, plus sorting and top-n operations.

Export data with pandas to csv, excel, json, dictionary or list, and other formats such as sql, clipboard, html, and markdown for reporting, sharing, and web development.

Explore Matplotlib, the Python plotting library, and learn how to visualize data with line graphs, bar charts, or pie charts to turn numbers into insights.

Explore Matplotlib's core components—pyplot, figure, axes, and axis—to quickly create charts, manage figures and axes, and customize axes with ticks, labels, and scaling.

Master NumPy basics for efficient numerical data analysis with Python. Create arrays, study shape and data types, index and slice, use broadcasting, and apply basic linear algebra for machine learning.

Predict product sales from tv advertising spend using simple linear regression. Build, train, and evaluate the model with pandas, numpy, matplotlib, and scikit-learn, and make predictions for new budgets.

Build a breast cancer classification model using 30 numeric features and logistic regression. Load data with pandas, visualize patterns, and evaluate with accuracy, precision, recall, f1, confusion matrix, and roc-auc.

Predict wine quality classification from real chemical measurements using a multi-class classifier. Build and evaluate a Python pipeline to classify wines as low, medium, or high quality.

Build a convolutional neural network to distinguish cats from dogs using images, loading data with TensorFlow datasets, preprocessing, training with Keras, and evaluating on train, validation, and test sets.

Train a YOLOv8 vehicle detection model on drone footage using the Drone Crossroad Tracking dataset in Roboflow and Colab, detecting cars and trucks with bounding boxes and confidence scores.

Learn to cluster credit card users by real financial activity using k-means, scaling, and imputation, then evaluate and visualize three customer segments with pca, elbow method, and silhouette scores.

Demonstrates how a machine learning pipeline bundles preprocessing steps—such as scaling and encoding—and the model into one object for predictions from raw data, contrasting manual vs pipeline deployment for production safety.

Predict bank term deposit subscriptions using a complete Python machine learning pipeline with pandas, preprocessing, logistic regression, and a random forest, evaluated via confusion matrices and ROC-AUC.

Automate web logins with Python and Selenium by building an auto login bot. Open a real browser, locate the username and password fields, type credentials, click login, and verify success.

Build a form auto filler with Python and Selenium to open a real website, find elements, type values, click buttons, and submit the form.

Build a multi-page web scraper with Python and BeautifulSoup that auto-navigates next pages, extracts text, authors, and tags, and saves results to a csv file.

Automate data consolidation and reporting with a Python data merger that loads multiple csv files, merges them, computes subtotals, and generates an Excel report with summaries and charts.

Build a camera-based rock-paper-scissors game powered by a YOLO model and Flask, where missions like win, lose, or draw drive rounds and scoring.

Create an interactive yoga pose mission game that uses live video, an AI pose model, and real-time detection to judge whether you hold the target pose during a countdown.