NLP Conversational AI is a hands-on, visual, and interactive repository for learning and experimenting with Natural Language Processing (NLP) techniques, focusing on building conversational AI systems. Explore a wide range of topics, from basic preprocessing to advanced feature engineering and model building, all through well-documented Jupyter notebooks and code samples.
- 📚 Educational Notebooks: Step-by-step lab sessions and tutorials on NumPy, Pandas, text preprocessing, feature extraction, and more.
- 🤖 Conversational AI Focus: Practical examples and code for building conversational agents and chatbots.
- 🔬 Data Science Workflows: End-to-end workflows for data loading, cleaning, feature engineering, and model evaluation.
- 🛠️ Hands-on Exercises: Interactive code cells and exercises for self-practice and experimentation.
- 📊 Visualization: Integrated visualizations and diagrams to aid understanding of data and algorithms.
- 🧩 Modular Structure: Each notebook is self-contained and focuses on a specific concept or technique.
- 💡 Beginner Friendly: Clear explanations and comments to help you learn and adapt the code for your own projects.
git clone <your-fork-or-clone-url>
cd nlp-conversational-aiInstall the required Python libraries:
pip install numpy pandas matplotlib scikit-learn nltk seaborn missingno plotlySome notebooks expect datasets in specific paths (e.g., C:/Machine Learning/ML_Datasets/).
Update the paths in the notebooks or place the datasets accordingly.
Open JupyterLab or Jupyter Notebook:
jupyter labNavigate to the notebooks/ directory and start exploring!
- NumPy & Pandas:
Learn the basics of numerical and tabular data manipulation. - Text Preprocessing:
Clean and prepare text data for NLP tasks. - Feature Engineering:
Extract and select features for machine learning models. - Model Building:
Implement and evaluate models for classification and regression. - Advanced Topics:
Outlier analysis, feature selection, and more.
- Data cleaning and missing value handling
- Exploratory data analysis and visualization
- Feature extraction and selection (Chi-square, Information Gain, Variance Threshold, Random Forest)
- Text preprocessing (tokenization, stemming, lemmatization)
- Building and evaluating machine learning models
- Decision trees, linear regression, and more
This project is licensed under the MIT License.
- Inspired by academic NLP courses and open-source data science communities.
- Uses datasets and libraries from the Python scientific ecosystem.
Contributions, issues, and feature requests are welcome!
Feel free to fork the repository and submit pull requests.