3. Weekly Highlights
Week 1 — Python & Git Foundations Arrange growth surroundings, mastered Git model management, accomplished first Python mission with Streamlit UI.
Week 2 — Knowledge Evaluation with Pandas & NumPy Realized information manipulation, cleansing strategies, and exploratory information evaluation on actual datasets.
Week 3–4 — Machine Studying Fundamentals (Peer Challenge 1) Studied supervised studying, educated classification and regression fashions, participated in first workforce peer mission on meals freshness detection utilizing CNN.
Week 5 — Superior ML (Ensemble, SVM, Regularization) Explored Random Forest, XGBoost, SVM, and regularization strategies. Realized mannequin analysis with F1, ROC-AUC, confusion matrices.
Week 6–7 — Deep Studying & Pc Imaginative and prescient (Hackathon) Constructed ANN and CNN architectures with TensorFlow/Keras. Participated in hackathon peer mission in workforce setting underneath time strain.
Github: https://github.com/maryam-ca/FoodGuard-AI-
Lindedln: https://www.linkedin.com/in/shahan-waheed-ba1667363/?skipRedirect=true
Week 8 — Unsupervised Studying Lined clustering, PCA, t-SNE, and market basket evaluation utilizing the Apriori algorithm.
Week 9 — Generative AI & LLMs Explored immediate engineering, zero-shot and few-shot studying, LLM functions, summarization, and RAG-based methods.
Week 10 — SRS & Challenge Planning (Activity 14) Wrote Software program Necessities Specification for last capstone mission. Chosen AI Resume Screener as solo mission.
Week 11 — Mannequin Growth & n8n Automation Constructed and educated Random Forest (89.92% accuracy) and ANN fashions. Developed full n8n automation workflow with GPT-4o-mini integration.
Week 12 — Deployment, Documentation & Reflection Deployed reside on Streamlit Cloud, printed Medium article, accomplished last report, presentation, and this fellowship story.
