Machine learning (ML) is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed. These systems learn and improve from the data. ML algorithms allow systems to automatically improve their performance on a specific task through experience and data analysis. Even though all machine learning systems can be categorized as artificial intelligence, but not all artificial intelligence systems are machine learning based.

Data: ML algorithms require large datasets for training. Data can be in various forms and can be from various sources such as databases, spreadsheets or APIs.

Training Data: Ml models are trained using data which they used to predict a specific outcome by recognizing patterns and relationships within the data.

Features: Features are the characteristics or attributes of the input data. They represent the information that the model uses to make predictions. Feature selection and engineering ensures that the model has the right input information.

Supervised Learning: In supervised learning, the model is trained on labeled data, where the correct output is provided for each input. Common algorithms include linear regression, decision trees, and neural networks.

Unsupervised Learning: Unsupervised learning deals with unlabeled data. The goal is to discover patterns, structure, or clusters within the data. Common techniques include clustering and dimensionality reduction.

Semi-Supervised Learning: This combines elements of both supervised and unsupervised learning. A model is trained on a small amount of labeled data and a large amount of unlabeled data.

Reinforcement Learning: In reinforcement learning, agents learn to make sequences of decisions in an environment to maximize a cumulative reward. It is commonly used in robotics, gaming, and autonomous systems.

Neural Networks: Neural networks are a class of ML algorithms inspired by the structure and function of the human brain. Deep neural networks, known as deep learning, have been particularly successful in various domains, including image and speech recognition.

Model Evaluation: After training, ML models need to be evaluated using test data to assess their performance. Common evaluation metrics include accuracy, precision, recall, F1-score, and more, depending on the problem.

Overfitting and Underfitting: Overfitting occurs when a model learns to perform well on the training data but does not generalize to new, unseen data. Underfitting, on the other hand, occurs when a model is too simple to capture the underlying patterns in the data.

Hyperparameter Tuning: Machine learning models often have hyperparameters (e.g., learning rate, number of layers) that need to be fine-tuned to optimize performance. Grid search and random search are common techniques for hyperparameter tuning.

Deployment: Once a model is trained and evaluated, it can be deployed in production systems to make predictions on new, real-world data.