Machine learning is a systematic process that transforms raw data into intelligent predictions and insights. The workflow consists of four main stages: data collection, data preprocessing, model training, and model evaluation. Each stage builds upon the previous one, creating a structured approach to developing effective machine learning solutions.
Data collection involves gathering information from multiple sources. Databases provide structured historical data, web APIs offer real-time information, files contain various formats of stored data, and IoT sensors capture live environmental measurements. All these sources feed into a central data repository for processing.
Data preprocessing involves multiple steps to prepare raw data for machine learning. First, data cleaning removes missing values, outliers, and inconsistencies. Then transformation normalizes and scales the data. Feature engineering creates meaningful variables, resulting in clean, structured data ready for model training.
Model training involves selecting the right algorithm for your problem type. Supervised learning uses labeled data for prediction tasks, unsupervised learning finds hidden patterns without labels, and reinforcement learning learns through trial and error. The training process transforms data through algorithms into predictive models.