🤖 AI & Machine Learning — Learning

An evolving collection of hands-on work as I learn AI and machine learning.
This project currently focuses on turning raw text into features that a machine-learning model can use.

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📝 NLP pipeline

The text-processing workflow takes raw text through a series of cleaning and feature-extraction steps:

flowchart TD
    A["Raw text"] --> B["Lowercase"]
    B --> C["Remove punctuation"]
    C --> D["Tokenization"]
    D --> E["Stopword removal"]
    E --> F["POS tagging"]
    F --> G["Lemmatization"]
    G --> H["TF-IDF"]
    H --> I["X: numerical features"]

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Output: X, a numerical feature matrix created with TF-IDF.

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🚀 Next steps

Use the feature matrix X together with target labels y to train and evaluate a model:

1. Prepare the data: pair X with y.
2. Train a model: fit a machine-learning model to the data.
3. Generate predictions: predict labels for the examples.
4. Evaluate performance: calculate the F1-score and inspect the confusion matrix.

X + y → Train model → Predictions → F1-score + Confusion matrix