• Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on developing algorithms and models that enable machines to learn from data and make predictions or decisions without being explicitly programmed.
  

• ML algorithms use statistical techniques to identify patterns and relationships in data and then use these patterns to make predictions or decisions on new, unseen data. The process of learning involves adjusting the parameters of the model based on feedback from the data, in a process known as training. There are three main types of machine learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
  

In supervised learning, the algorithm learns from labeled data, where the outcome or target variable is known. In unsupervised learning, the algorithm learns from unlabeled data, where the goal is to identify patterns and relationships in the data. In reinforcement learning, the algorithm learns through trial and error by interacting with an environment and receiving feedback in the form of rewards or penalties.

Machine learning has a wide range of applications, from image and speech recognition, to natural language processing, recommender systems, fraud detection, and predictive maintenance. It is increasingly being used in industry and research, and has the potential to transform many aspects of our lives.

Deep learning is a branch of machine learning that includes deep ML models. Neural networks, the name for these deep models, are particularly well suited for fields like computer vision or Natural Language Processing (NLP). Discriminative models, whose goal is to make predictions or infer patterns from data, are frequently mentioned when we talk about machine learning and deep learning (ML and DL) models.

Generative AI, a further sub-branch of DL, uses powerful Neural Network models, typically Transformers, to generate brand new content, from images to natural language, from music to video.