Python for Beginners: Learn Modern Python

Master the fundamentals of Python, including syntax, snake_case naming, and data types (int, float, bool, str). Explore dynamic typing, type conversion, operators, control flow, and string formatting with f strings.

Explore python data structures: lists, tuples, sets, and dictionaries, and their mutability, order, and lookup, plus comprehensions for these collections with python tutor visualizations.

Master functions in Python by defining with def, passing parameters and arguments, returning values, using default and keyword arguments, and understanding scope, lifetime, and indentation.

Master modules and packages in Python by creating and importing custom modules, using predefined and external packages via pip and PyPI, and improving code readability with docstrings and pep8 guidelines.

Explore object-oriented programming by defining classes and creating objects with init constructors. Learn instance attributes and methods, inheritance, and method overriding, plus class and static methods in Python.

Master object oriented programming concepts with UML diagrams, class diagrams, encapsulation, polymorphism, public vs private attributes, getters and setters, and method overloading and overriding in Python.

Master file handling of text and binary files, including read, write, and append with open and mode options. Learn robust error handling with try, except, finally, and custom exceptions.

Learn debugging techniques in Python, including print statements, the Python debugger (pdb), IDE breakpoints, and logging, with hands-on examples in Jupyter Notebook and discussions of recursion and factorial concepts.

Explore virtual environments and testing tools, compare unit testing with pytest, and learn the basics of ci/cd to support reproducible, clean Python development.

Learn continuous integration and continuous deployment with automated testing using GitHub actions and GitLab CI/CD. Build and validate Python projects with a workflow defined in a yml file.

Explore virtual environments, testing, and CI/CD basics, then master Python code quality with linting using Flake8 and Black, including style checks, formatting, and unused imports.

Learn numpy arrays and vectorized operations for data analysis, then use pandas Series and DataFrame to clean, filter, read and write csv or excel files, and group data.

Learn data visualization in Python with Matplotlib and Seaborn, plotting histograms, bar charts, and line plots with KDE for distribution insights. Explore exploratory data analysis, pandas workflows, and chunking datasets.

Fix and demonstrate corrected code from the previous lecture for scatter plots and distributions using Iris and Olympics data, showcasing seaborn hist plots, kde toggle, and basic exploratory data analysis.

Introduction to machine learning and ai with python, using scikit-learn to perform train-test splits, train models, and evaluate with metrics like accuracy, precision, recall, and f1.

Learn the basics of machine learning with Python, including evaluating models with weighted precision, recall, and f1; compare logistic regression, linear regression, and decision trees, and master the data-to-deployment workflow.

Explore machine learning and AI with Python, covering deep learning, neural networks, and frameworks like TensorFlow and PyTorch, plus no-code platforms for building and deploying models.

Learn to build a simple machine learning model in Python for beginners using scikit-learn's decision tree to classify iris species, with training/testing split and a visualization.

Learn web development with Python by exploring Flask and Django, then build a simple Flask app with templates, handling form input and updating an items list.

Learn the fundamentals of REST API design with Python, covering stateless client-server interactions, HTTP methods, JSON data exchange, and testing with Postman and Insomnia.

Learn fast API, a high performance web framework for building HTTP based APIs in Python, run with uvicorn, and explore Docker deployment to cloud platforms like Heroku, AWS, and Azure.