What ML is?

Machine Learning (ML) is a field of artificial intelligence (AI) that involves the development of algorithms and statistical models that enable machines to learn from data, without being explicitly programmed. It is a method of teaching computers to learn from experience and improve their performance over time.

In simple terms, ML is the ability of machines to learn from data, recognize patterns and make predictions or decisions. ML algorithms use statistical techniques to enable computers to learn from data, identify patterns and generalize from those patterns to new data.

There are several types of ML, and the most common ones are:

• Supervised learning: It trains a model on labeled data, where the goal is to predict the output based on input.

• Unsupervised learning: It trains a model on unlabeled data, where the goal is to find patterns or structure in the data.

• Reinforcement learning: It trains a model by providing rewards and penalties in a specific context, where the goal is to maximize the cumulative reward.

Overall, ML is a rapidly growing field that is being applied to a wide range of areas such as natural language processing, computer vision, speech recognition, and many other areas. It is a key technology that enables machines to learn from data and improve their performance over time.

Machine Learning (ML) is based on an inductive approach. In an inductive approach, the system starts with observations or data, and uses it to learn from examples and discover patterns. The goal is to generalize from the examples and make predictions or decisions about new unseen data. Inductive learning is based on the idea of learning from experience, just like humans do.

In Machine Learning, the system is trained on a dataset, which contains input-output pairs, called labeled data. The training process allows the model to learn the relationship between input and output, and then to generalize that relationship to new unseen data. After the model has been trained, it can be used to make predictions or decisions on new unseen data.

In summary, ML is a data-driven approach, where the system starts with data and uses it to learn from examples, discover patterns and make predictions or decisions about new unseen data. It is based on the idea of learning from experience and generalizing from examples, which is the core of the inductive approach.