Scalable ML Algorithms
Master scalable machine learning algorithms and techniques for processing large datasets. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples
45 min•By Priygop Team•Last updated: Feb 2026
Distributed ML Algorithms
- Distributed Linear Regression: Scale linear models
- Distributed Random Forest: Parallel tree training
- Distributed K-means: Parallel clustering
- Distributed Neural Networks: Parallel neural network training
Federated Learning
- Local Training: Train on local data
- Model Aggregation: Combine local models
- Privacy Preservation: Keep data local
- Communication efficiency: Minimize data transfer
Online Learning
- Incremental Learning: Update models with new data
- Stochastic Gradient Descent: Online optimization
- Adaptive Learning: Adjust to changing data
- Memory Management: Handle streaming data
Model Parallelism
- Data Parallelism: Distribute data across nodes
- Model Parallelism: Distribute model across nodes
- Pipeline Parallelism: Parallel processing stages
- Hybrid Parallelism: Combine multiple strategies