Coding Projects by Kieran

AI-Powered Image-to-Text Conversion Project – Part 2

Students will apply AI-powered tools to generate captions, descriptions, and summaries from images. By expanding on a simple image caption, they will utilize a text generation model (GPT-2) and a text-to-image model (Stable Diffusion) to create a complete, interactive workflow for text-to-image conversion. They will practice generating descriptive text, summarizing the content, and exploring creative applications.

My Voice, My Words: Build Your Own Voice-to-Text Assistant

To reinforce the core concepts of audio input, digitization, and speech recognition using a Python-based audio recorder that converts your speech into text and visualizes the waveform.

“Say & See” – Your Mini Speech-to-Text Studio

Re-apply today’s concepts of audio capture, digitization, and speech recognition by building a self-contained Python tool that records your voice, transcribes it with Google Speech-to-Text, and plots its waveform.

Voice Master+: Extend Your Talking AI

To reinforce core concepts of Text-to-Speech (TTS), user input handling, randomness, and command interpretation by upgrading the original AI Voice Lab into a more interactive and responsive application.

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In this assignment, you will build a complete console-based voice translation application using speech recognition, Google Translate, and text-to-speech. The program takes spoken English input, converts it to text, translates it into a user-selected language, and then speaks the translated result.

Smart Command Pro: Upgrade Your Voice Assistant

To reinforce all key concepts taught in the lesson, including continuous voice interaction, command recognition, TTS responses, error handling, and working with date/time — by enhancing the basic assistant with smarter and more customized responses.

Real-Time Speech Translation Application with Multiple Languages

Extend your speech translation application to handle multiple languages dynamically. Allow users to select the source and target language for real-time translation, and improve error handling for speech recognition.

Exploring AI Learning Styles: Zero-Shot, One-Shot & Few-Shot Mastery

To help students practically understand how the level of context and examples (zero, one, few) influences AI’s accuracy and creativity in performing classification and text generation tasks.

Penguin Research Data Cleaning Tool

In this assignment, you will clean the built-in Penguins dataset available through Seaborn. You will identify missing values, count them column by column, visualize them using a heatmap, remove completely empty records, fill missing categorical values, and estimate missing numerical values using interpolation, backward fill, and forward fill.

Monthly Savings Progress Chart

In this assignment, you will build a Monthly Savings Progress Chart using Python, NumPy, and Matplotlib. You will plot two savings plans across twelve months, compare their growth, control the visible axis ranges, add a legend, and shade the difference between the plans.

Fitness Progress Comparison Chart

In this assignment, you will build a Fitness Progress Comparison Chart using Seaborn's exercise example dataset. You will filter running records, compare pulse readings after 1 minute and 30 minutes, merge the results, calculate pulse change, sort the participants, build a grouped bar plot, and label each bar.

Visualization Using Histogram

Students have to visualize data using histograms to check the distribution of data.