ML Engineering Portfolio
Coursework and projects completed through the Interview Kickstart ML Engineering program — spanning classical ML, deep learning, NLP, transformers, and production LLM systems (RAG, fine-tuning, LoRA/DPO). Each notebook includes detailed reasoning, analysis, and key takeaways — not just code.
Overview
Modules
| # | Module | Key Topics | Notebook |
|---|---|---|---|
| 1 | Build Your First ML Model | Linear/Ridge/Lasso regression, feature scaling, cross-validation | Open |
| 2 | Model Evaluation & Interpretation | Precision/Recall/F1, ROC-AUC, confusion matrix, SHAP | Open |
| 3 | Model Optimization | Grid Search, Random Search, Optuna (Bayesian), ensembles | Open |
| 4 | Neural Networks Basics | PyTorch MLP, BatchNorm, Dropout, activation functions | Open |
| 5 | ML Architectures | CNN for images, BiLSTM for text, architecture selection | Open |
| 6 | Transformer Based Models | Self-attention, multi-head attention, transformer blocks | Open |
| 7 | Deep Dive into LLMs | Prompt engineering, LoRA/PEFT, RAG, fine-tuning (SFT/DPO) | Open |
Projects
| Project | Score | Description |
|---|---|---|
| Customer Churn Prediction | 92/100 | End-to-end churn model: feature engineering → XGBoost → Optuna tuning |
| Medical Text Classification | 88/100 | Clinical NLP pipeline: TF-IDF → SVM → BERT fine-tuning |
| Capstone: RAG Pipeline | 95/100 | Production RAG: hybrid retrieval, re-ranking, RAGAS evaluation |
Completion Summary
- 7/7 modules completed with detailed analysis and reasoning
- 2 applied projects scored by industry experts (92%, 88%)
- 1 Capstone project with production-grade RAG pipeline (95%)
How to Run
# Clone the repository
git clone https://github.com/leoniscode/ml-engineering-portfolio.git
cd ml-engineering-portfolio
# Install dependencies
pip install -r requirements.txt
# Launch Jupyter and open any notebook
jupyter notebook
Individual projects have their own requirements.txt for project-specific dependencies:
cd projects/capstone-rag-pipeline
pip install -r requirements.txt
Tech Stack
Core ML: Python, NumPy, pandas, scikit-learn, XGBoost, Optuna
Deep Learning: PyTorch, HuggingFace Transformers, PEFT/LoRA
NLP & LLMs: TF-IDF, BERT, sentence-transformers, LangChain
RAG & Retrieval: FAISS, BM25, cross-encoder re-ranking
Evaluation: SHAP, RAGAS, DeepEval, Evidently AI
Visualization: Matplotlib, Seaborn