This is a curated, self-directed journey toward becoming a Human-Centered Machine Learning Engineer — a role at the intersection of data science, human factors, and responsible AI. The focus is on developing robust technical skills and human-centered intuition for building ML systems that are usable, interpretable, and ethically sound.
This repository is structured into focused learning "blocks," each with Jupyter notebooks, hands-on projects, and curated readings. The goal is to build deep technical understanding and the human-centered thinking needed to design useful, ethical, and interpretable ML systems.
| Path | Description |
|---|---|
Block1/ |
Python fundamentals and environment setup |
Block2/ |
Data manipulation with Pandas |
block3_intro_to_ml/ |
Intro to Machine Learning: Regression & Classification |
block4_human_ai/ |
Human-AI Interaction & Interpretability |
block5_mini_project/ |
Mini ML Projects, Modeling, UX, Prototyping, Ethics |
block6_intro_to_deep_learning/ |
Neural networks and deep learning fundamentals |
block7_deep_learning_tabular_data/ |
Deep learning with tabular data and embedding models |
block8_data_visualization/ |
Communicating ML insights through Python visualization and design |
block9_human_centered_design/ |
Fundamentals of UX and human-centered design for ML solutions |
block12_XAI/ |
Explainable AI techniques: SHAP, LIME, PDP |
environment.yml |
Reproducible Conda environment |
HCMLE.code-workspace |
VS Code workspace for auto-loading settings |
README.md |
Project goals, learning blocks, and setup instructions |
.gitignore |
Ignore notebooks, data, and system clutter |
| Block | Focus | Status |
|---|---|---|
| 1 | Python Fundamentals & Environment Setup | Completed |
| 2 | Data Manipulation with Pandas | Completed |
| 3 | Intro to Machine Learning: Regression & Classification | Completed |
| 4 | Human-AI Interaction & Interpretability | Completed |
| 5 | Mini ML Project | Completed |
| 6 | Introduction to Deep Learning | Completed |
| 7 | Deep Learning for NLP or Tabular Data | Completed |
| 8 | Data Visualization & Communication | Completed |
| 9 | Human-Centered Design Fundamentals | In Progress |
| 12 | Explainable AI Techniques | In Progress |
Each block includes:
notebooks/– clean and commented Jupyter notebooksdata/– sample datasets (if applicable)README.md– block-specific goals and progressmodel_card.md– model transparency summaryproject_schedule.md– daily task plan for hands-on blocks
To run this project locally with Jupyter + Conda + VS Code:
git clone https://github.com/flyboi96/HCMLE.git
cd HCMLEMake sure you have Anaconda or Miniconda installed.
conda env create -f environment.yml
conda activate hcmle-envTo launch the project with the correct kernel, folders, and settings:
code HCMLE.code-workspaceMake sure you have the Python and Jupyter extensions installed in VS Code.
If you add new packages:
conda env update -f environment.yml --pruneTo export your environment:
conda env export --from-history > environment.ymlI’m using a GitHub Project Board and Issues to track tasks and milestones for each block. This keeps everything visible, organized, and reviewable over time.
- Python 3.11
- Pandas, NumPy, Matplotlib, Seaborn
- scikit-learn
- JupyterLab & VS Code Notebooks
- Git, GitHub, Conda
- Markdown, GitHub Project Boards
- Python Data Science Handbook by Jake VanderPlas
- Designing with the Mind in Mind by Jeff Johnson
- Real-world datasets (e.g., Titanic, Iris, UCI ML Repository)
- People + AI Guidebook by Google PAIR
- Storytelling with Data by Cole Nussbaumer Knaflic
- Interpretable Machine Learning by Christoph Molnar
- Kaggle & DataCamp tutorials on Explainability (LIME, SHAP)
This project is open-source under the MIT License. Feel free to fork, adapt, or use this project as inspiration for your own ML learning journey.