What is Machine Learning?
Machine Learning allows systems to generalize from examples. For instance, an ML algorithm trained on thousands of email samples learns to classify future messages as “spam” or “not spam” based on statistical patterns.
ML models are trained using large datasets and optimized by adjusting internal parameters to minimize error. Over time, the model “learns” to improve accuracy as it processes more data. Machine Learning spans a spectrum - from simple regression models to deep neural networks powering Generative AI and Large Language Models (LLMs).
Instead of relying on hard-coded rules, ML models identify relationships within datasets and make predictions, classifications, or decisions automatically. Machine Learning powers applications like recommendation engines, spam filters, predictive maintenance, and fraud detection—and forms the foundation of modern AI systems.
How Machine Learning Works
- Data Collection: Gathering relevant, labeled or unlabeled data.
- Data Preparation: Cleaning, normalizing, and splitting datasets into training and testing sets.
- Model Selection: Choosing an algorithm (e.g., regression, tree-based, neural network).
- Training: The model iteratively learns patterns from training data.
- Evaluation: Testing model accuracy using unseen data.
- Deployment: Integrating the trained model into applications or workflows.
- Monitoring & Retraining: Continuously improving as new data becomes available.
