🧠 ML Labs

Welcome to ML Labs, a hands-on repository where I document and keep track of my learning process in Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). This repo includes all the practical exercises, code notebooks, and projects tackled throughout my learning journey.

📚 What This Repo Covers

• 📌 Foundational ML concepts (Supervised, Unsupervised Learning, etc.)
• 📊 Data Preprocessing and Feature Engineering
• 🤖 Model Training and Evaluation (Scikit-Learn, StatsModels)
• 🧮 Mathematical foundations (Linear Algebra, Probability, Calculus)
• 🧠 Deep Learning with TensorFlow and PyTorch
• 🗣️ Natural Language Processing (Text Classification, Tokenization, Embeddings)
• 🧪 Real-world projects (Healthcare, Finance, E-Commerce)
• 🧰 Tools: Jupyter, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch

This repo is a practical journey through both theory and real-world implementation.

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🚀 Getting Started

1. Clone the repo

git clone https://github.com/greatdaveo/ML-Labs.git
cd ML-Labs

2. Set up a virtual environment

Using Conda:

conda create -p venv python=3.10
conda activate venv/

Or using virtualenv:

python3 -m venv .venv
source .venv/bin/activate  # For macOS/Linux
.venv\Scripts\activate     # For Windows

3. Install dependencies

pip install -r requirements.txt

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🏆 Goals

• To build a strong theoretical and practical understanding of ML/DL/NLP
• Create a solid portfolio of ML projects
• Develop end-to-end ML pipelines with real-world data

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📜 License

This project is for educational purposes. Notebooks and scripts are adapted for personal learning and practice.

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🙋‍♂️ About Me

Hi, I'm David Olowomeye — a passionate software developer learning and applying AI/ML techniques to real-world problems.

Feel free to connect:

• LinkedIn
• GitHub

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🌱 Future Additions

• MLOps pipelines (MLflow, model versioning, deployment)
• Streamlit dashboards
• TensorBoard visualization
• More real-world datasets and Kaggle challenges