Authors: Nwokike Onyeka, Obinwa Ogechi Perpetual
Course: AI Tools and Applications
Institution: [Power Learn Project Academy]
Date: October 2025
This repository contains my complete submission for the AI Tools Assignment on the theme “Mastering the AI Toolkit.”
The project demonstrates understanding and practical use of modern AI frameworks — TensorFlow, PyTorch, Scikit-learn, and spaCy — across theory, implementation, and ethics.
| Part | Description | Deliverable |
|---|---|---|
| Part 1 | Theoretical Understanding of AI tools | Theoretical_Answers.md |
| Part 2 – Task 1 | Classical ML using Scikit-learn (Iris Classifier) | Iris Classifier Notebook |
| Part 2 – Task 2 | Deep Learning using TensorFlow (MNIST CNN) | MNIST CNN Notebook |
| Part 2 – Task 3 | NLP using spaCy (Entity & Sentiment Extraction) | NLP Task |
| Part 3 | Ethical Analysis and Reflection | Ethical_Reflection.md |
- TensorFlow – for deep learning and CNN model building.
- Scikit-learn – for classical machine learning (Decision Tree Classifier).
- spaCy – for NLP tasks like Named Entity Recognition and Sentiment Analysis.
- Jupyter Notebook / Google Colab – for experimentation and visualization.
- GitHub – for version control and project submission.
- Algorithm: Decision Tree Classifier
- Accuracy: ~97%
- Evaluation Metrics: Accuracy, Precision, Recall
- Model: Convolutional Neural Network
- Test Accuracy: >99%
- Output: Classification of handwritten digits (0–9)
- Task: Named Entity Recognition and Rule-Based Sentiment Analysis
- Entities Extracted: Product Names, Brands
- Sentiment Output: Positive / Negative summary
This project emphasizes responsible AI use — addressing bias, fairness, transparency, privacy, and human accountability.
See the full write-up: Ethical_Reflection.md
This project demonstrates the practical application of AI frameworks in machine learning, deep learning, and NLP — combined with ethical awareness.
It fulfills all parts of the AI Tools Assignment and serves as a foundation for future AI engineering projects.
💡 “Small wins lead to big successes — test code incrementally and think ethically.”