/Introductory-Data-Science

Hands-on machine learning tutorials in Google Colab, covering various algorithms and techniques for learners at different levels.

Primary LanguageJupyter NotebookMIT LicenseMIT

🤖 Machine Learning Tutorials 📚

This repository contains a series of comprehensive machine learning tutorials implemented in Google Colab. These tutorials cover a wide range of topics in machine learning, from basic data preprocessing to advanced deep learning techniques.

📋 Contents

  1. Data Preprocessing: Introduction to data retrieval, inspection, cleaning, and splitting.
  2. Classification: Implementing and comparing various classification algorithms (Logistic Regression, Decision Tree, Random Forest, SVM) on the Breast Cancer Wisconsin dataset.
  3. Regression: Exploring different regression techniques (Linear, Polynomial, Lasso, Ridge) using the California Housing Prices dataset.
  4. Unsupervised Learning: Applying clustering (K-means, Hierarchical) and dimensionality reduction (PCA, t-SNE) techniques on the Iris dataset.
  5. Multi-Layer Perceptron (MLP): Building and training an MLP for image classification using the MNIST dataset.
  6. Convolutional Neural Networks (CNN): Implementing a CNN for image classification on the CIFAR-10 dataset.
  7. Recurrent Neural Networks (RNN): Comparing different RNN architectures (Simple RNN, LSTM, GRU) for sentiment analysis on the IMDB dataset.

👥 Who is this for?

These tutorials are designed for:

  • Students and practitioners of machine learning and data science
  • Beginners looking to gain hands-on experience with various ML techniques
  • Intermediate learners aiming to expand their knowledge and skills
  • Anyone interested in understanding the practical implementation of machine learning algorithms

✨ Features

  • Step-by-step implementations in Google Colab
  • Comprehensive coverage of data preprocessing, model training, and evaluation
  • Visualization of results for better understanding
  • Comparison of different algorithms and architectures
  • Practical tips and best practices for each technique

🚀 How to Use

  1. Clone this repository or open the notebooks directly in Google Colab.
  2. Follow the tutorials in order, or pick the ones most relevant to your learning goals.
  3. Run the code cells and experiment with the parameters to deepen your understanding.
  4. Use these tutorials as a reference for your own projects or further learning.

🛠️ Requirements

All tutorials are designed to run in Google Colab, which provides all necessary libraries and GPU acceleration. However, if you wish to run them locally, ensure you have the following installed:

  • Python 3.x
  • PyTorch
  • scikit-learn
  • numpy
  • pandas
  • matplotlib
  • seaborn

🤝 Contributions

Contributions, bug reports, and feature requests are welcome! Feel free to open an issue or submit a pull request.

📄 License

MIT license.

Happy Learning! 🎓