Machine learning (ML) and deep learning (DL) are essential for intelligent systems that offer artificial intelligence (AI) capabilities. ML enables systems to learn from data to automate analytical model building and solve tasks, while DL, based on artificial neural networks (ANNs), often outperforms traditional methods. This article explains the fundamentals of ML and DL, highlighting their differences, processes, and challenges in electronic markets. ML includes supervised, unsupervised, and reinforcement learning, while DL uses deep neural networks with multiple layers for complex pattern recognition. DL is particularly effective for high-dimensional data like text, images, and audio, but may lack interpretability. Challenges include data bias, concept drift, and the need for explainability in AI decisions. ML and DL models require careful selection of hyperparameters, data preprocessing, and model evaluation to ensure accuracy and efficiency. Transfer learning allows models to be adapted to new tasks with limited data, but risks of bias and adversarial attacks remain. The article also discusses the importance of ethical considerations, data privacy, and the need for transparent AI systems. As AI becomes more integrated into electronic markets, understanding ML and DL is crucial for developing effective, reliable, and ethical intelligent systems.Machine learning (ML) and deep learning (DL) are essential for intelligent systems that offer artificial intelligence (AI) capabilities. ML enables systems to learn from data to automate analytical model building and solve tasks, while DL, based on artificial neural networks (ANNs), often outperforms traditional methods. This article explains the fundamentals of ML and DL, highlighting their differences, processes, and challenges in electronic markets. ML includes supervised, unsupervised, and reinforcement learning, while DL uses deep neural networks with multiple layers for complex pattern recognition. DL is particularly effective for high-dimensional data like text, images, and audio, but may lack interpretability. Challenges include data bias, concept drift, and the need for explainability in AI decisions. ML and DL models require careful selection of hyperparameters, data preprocessing, and model evaluation to ensure accuracy and efficiency. Transfer learning allows models to be adapted to new tasks with limited data, but risks of bias and adversarial attacks remain. The article also discusses the importance of ethical considerations, data privacy, and the need for transparent AI systems. As AI becomes more integrated into electronic markets, understanding ML and DL is crucial for developing effective, reliable, and ethical intelligent systems.