Machine Learning (ML)

Machine Learning involves designing and building intelligent models that enable systems to learn from data, identify patterns, and make predictions or decisions without explicit programming. ML applications span numerous industries, from healthcare and finance to retail and autonomous vehicles. Developers use techniques such as supervised, unsupervised, and reinforcement learning to train algorithms on vast datasets, enabling automated insights and process improvements. ML models power recommendation engines, fraud detection, image recognition, natural language processing, and predictive analytics. Implementing machine learning requires expertise in data preprocessing, feature engineering, model selection, and performance tuning. Additionally, deploying ML models in production involves considerations for scalability, latency, and continual learning. The ultimate goal of ML is to automate complex tasks, reduce human intervention, and support data-driven decision-making that improves efficiency and business outcomes.