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As a senior software engineer, I have witnessed the rapid growth and popularity of machine learning (ML) and deep learning (DL) techniques, particularly in the context of Large Language Models (LLMs) and Generative Pre-trained Transformers (GPTs).
Here we explore the key differences between machine learning and deep learning, focusing on the critical skills needed to excel in these domains.
Machine learning is a subset of artificial intelligence that enables computer systems to learn and improve from experience without being explicitly programmed. It encompasses a wide range of algorithms and techniques that allow machines to identify patterns, make predictions, and take actions based on data.
The primary goal of machine learning is to develop models that can generalize well to unseen data. This is achieved through various approaches, such as supervised learning, unsupervised learning, and reinforcement learning. Some of the essential skills required for machine learning include: