Data Mining (DM) is the core stage of the knowledge discovery process that is aim to extract interesting and potentially useful information from data [Goodfellow 2016] [Mierswa 2017]. As the DM techniques and businesses evolved, there is a need for data analysts to better understand and standardise the knowledge discovery process. DM can serve as a foundation for Artificial Intelligence and Machine Learning. The term "data mining", as described in this document, is meanly oriented to large-scale data mining. However, many techniques that work for large-scale datasets can work also for small data.

Artificial Intelligence (AI) is any technique that enables computers to mimic human behaviour, including machine learning, Natural Language Processing (NLP), language synthesis, computer vision, robotics, sensor analysis, optimization and simulation, and many more.

Machine Learning (ML) is a subset of AI techniques that enables computer systems to learn from previous experience and improve their behaviour. ML techniques include Support Vector Machines (SVM), decision trees, Bayes learning, k-means clustering, association rule learning, regression, neural networks, and many more.

Neural Networks (NNs) or Artificial Neural Networks (ANNs) are a subset of ML techniques, which are loosely inspired by biological neural networks included Deep Learning. They are usually described as a collection of connected units, called artificial neurons and organised in layers.

Deep Learning (DL) is a subset of NNs that makes the computational multi-layer NN feasible. Typical DL architectures are deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), deep belief networks (DBNs), and many more.