Python Mastery with Generative AI: Coding to AI Integration

Learn to integrate AI code completion tools in Python, generate and complete programs (including a Fibonacci sequence), and perform data scraping with BeautifulSoup and requests using AI-assisted workflows.

Explore how a tool based on artificial intelligence analyzes and optimizes a lengthy factorial function, converting verbose code into a memoized, recursive solution with intermediate values.

Learn to use AI tools to debug Python code by submitting snippets to ChatGPT, identify indentation and premature return errors in loops, and document fixes.

Design and test a Python calculator with add, subtract, multiply, and divide, including input handling and error checks. The lecture uses AI-powered code review to analyze quality and suggest improvements.

Learn advanced Python environment management by creating and activating virtual environments, understanding key files (include, lib, bin, scripts) and activation scripts across platforms for isolation and reproducibility.

Profile and optimize Python code using timing, line profiling, and memory insights with generative AI. Learn practical profiling steps and how AI can assist code optimization.

Learn how to debug Python in VS Code using run and debug extension, set breakpoints, inspect errors, and step through code to fix exceptions and optimize performance.

Explore lambda functions and functional programming in Python, including anonymous functions, higher-order functions, map and filter, sorting, and use cases with events, callbacks, and asynchronous programming.

Explore threading, multiprocessing, and asynchronous programming in Python, learning how to run tasks concurrently, distribute workloads across CPUs, and implement non-blocking, event-driven programs.

Explore how Python uses exception handling with try/except to manage errors and invalid input, prevent crashes, and support input validation, debugging, and custom exceptions.

Discover memory-efficient Python patterns, including on-the-fly Fibonacci generation and in-place string processing, to optimize performance and reduce copies, with practical prompts and code demonstrations.

Explore design patterns in python, including creational patterns, and pyramid, diamond, number, and alphabetic patterns; learn to implement, customize, and visualize these patterns with code and problem solving concepts.

Master python best practices, including descriptive naming, list and generator expressions, and avoiding magic numbers, while embracing docstrings, error handling, and unit testing for reliable, maintainable code.

Explore unit testing and test-driven development in Python with Gemini, demonstrate testing code, installing modules, and handling errors while using Gemini to convert audio formats and transcribe speech.

Learn advanced data manipulation with pandas by loading diverse data sources, cleaning duplicates, imputing missing values, and preparing datasets for analysis with numpy and dask.

Explore data visualization techniques in Python, creating line, bar, pie charts, scatter plots, and heat maps. Compare use cases and limitations to select appropriate charts.

Elaborate data sets and materialize their visualization using Python, pandas, and matplotlib, retrieving finance data from Kaggle and describing statistics like min, max, and distribution plots.

Explore design principles for clean, maintainable Python code using Copilot, including solid principles—single responsibility, open/closed, Liskov substitution, interface segregation, and dependency inversion—with practical code examples.

Explore advanced object oriented programming techniques in Python, including design patterns, data models via special methods, metaprogramming and dynamic access, plus implementing container protocols for custom iterables.

Explore dynamic attributes and methods in Python, learning how to add, set, get, and delete attributes at runtime and make objects flexible and adaptable.

Explore data science and machine learning with a linear regression example, synthetic data, train-test split, model training and plotting, and using generative AI to generate code.

Master Python network programming by using the socket module for client-server communication, http client and ftp lib for web and file transfers, and async io for scalability.

Advance web development with Python by building a Flask site, integrating AI, ML, data analysis and visualization, and using frameworks like Django and Next.js with templates and dynamic routes.

Explore deep learning concepts and frameworks in Python, including neural networks, Keras, PyTorch, OpenCV for image tracking, and RNN and LSTM architectures, with practical prompts and model training insights.

Explore natural language processing with Python, using NLTK for tokenization, stopword removal, and normalization, then apply POS tagging, NER, sentiment analysis, and build chatbots, translation, and summarization tools.

Build a Flask-based to-do list app using Python and JavaScript, from environment setup to a web interface with templates, CSS, and AI-assisted code generation and prompts.