10-machine-learning-foundations

Machine Learning Foundations Tutorials

This directory contains tutorials on fundamental machine learning concepts and algorithms using IBM Watsonx and popular ML frameworks.

Prerequisites

Each tutorial in this directory includes its own setup and installation instructions. Please refer to the individual tutorial files for specific requirements.

Common requirements:

• Python 3.10 - 3.13
• IBM watsonx.ai account

Quick Start

1. Install dependencies (see Installation above)
2. Navigate to this directory:

   cd tutorials/10-machine-learning-foundations

3. Open and run your first tutorial

Key Concepts

Machine Learning Types

• Supervised Learning: Learn from labeled data (classification, regression)
• Unsupervised Learning: Find patterns in unlabeled data (clustering, dimensionality reduction)
• Reinforcement Learning: Learn through interaction and rewards

Common Algorithms

• Classification: Logistic Regression, Decision Trees, Random Forest, SVM, Neural Networks
• Regression: Linear Regression, Ridge, Lasso, Gradient Boosting
• Clustering: K-Means, DBSCAN, Hierarchical Clustering

ML Workflow

1. Problem Definition
2. Data Collection & Preprocessing
3. Feature Engineering
4. Model Selection & Training
5. Evaluation & Tuning
6. Deployment & Monitoring

Common Use Cases

• Predictive Analytics: Customer churn, sales forecasting, demand prediction
• Classification: Spam detection, sentiment analysis, image recognition
• Clustering: Customer segmentation, anomaly detection, document clustering
• Recommendation Systems: Collaborative filtering, content-based filtering

Additional Resources

• Main Repository README
• IBM Watsonx Documentation
• Scikit-learn Documentation
• PyTorch Tutorials

Contributing

Found an issue or want to add a new ML tutorial? See our Contributing Guide.

License

See the LICENSE file in the repository root.