Advanced – AI (Artificial Intelligence) & ML (Machine Learning), the focus shifts towards advanced techniques and applications. This includes delving into deep learning architectures like convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) for sequential data analysis. Advanced practitioners often explore reinforcement learning for autonomous decision-making systems and generative adversarial networks (GANs)
for generating synthetic data. They may also specialize in natural language processing (NLP), robotics, or computer vision, pushing the boundaries of AI & ML to solve complex problems and create innovative solutions across diverse domains.