The process of automatically recognising significant patterns within large amounts of data is called "machine learning." Throughout the last couple of decades, it has evolved into a tool used in almost every activity requiring the extraction of information from large data sets. We are surrounded by technology that is based on machine learning: Search engines are learning how to bring us the best results (while placing profitable ads), antispam software is learning how to filter our email messages, and credit card transactions are secured by software that learns how to detect frauds. Intelligent personal assistance software on smartphones can learn to recognise voice commands, and digital cameras can train themselves to identify faces. Accident-prevention systems in vehicles are constructed with the help of machine-learning algorithms. These systems are installed in modern automobiles. In addition, machine learning is extensively utilised in various scientific applications, including bioinformatics, medicine, and astronomy. One aspect that is shared by all of these applications is the fact that, in contrast to more conventional applications of computers, in these situations, due to the complexity of the patterns that need to be detected, a human programmer is unable to provide an explicit, fine-detailed specification of how such tasks should be carried out. This is one of the characteristics that make all of these applications unique. Taking cues from other intelligent beings, most of our capabilities have been obtained or improved via learning from our experiences (rather than following explicit instructions). Tools for machine learning are used to give computer programmes the capacity to "learn" and modify their behaviour on their own. The first objective of this book is to provide the fundamental ideas that comprehensively underpin machine learning while still being simple to understand. The process of automatically recognising significant patterns within large amounts of data is called "machine learning." Throughout the last couple of decades, it has evolved into a tool used in almost every activity requiring the extraction of information from large data sets. We are surrounded by technology that is based on machine learning: Search engines are learning how to bring us the best results (while placing profitable ads), antispam software is learning how to filter our email messages, and credit card transactions are secured by software that learns how to detect frauds. Intelligent personal assistance software on smartphones can learn to recognise voice commands, and digital cameras can train themselves to identify faces. Accident-prevention systems in vehicles are constructed with the help of machine-learning algorithms. These systems are installed in modern automobiles. In addition, machine learning is extensively utilised in various scientific applications, including bioinformatics, medicine, and astronomy. One aspect that is shared by all of these applications is the fact that, in contrast to more conventional applications of computers, in these situations, due to the complexity of the patterns that need to be detected, a human programmer is unable to provide an explicit, fine-detailed specification of how such tasks should be carried out. This is one of the characteristics that make all of these applications unique. Taking cues from other intelligent beings, most of our capabilities have been obtained or improved via learning from our experiences (rather than following explicit instructions). Tools for machine learning are used to give computer programmes the capacity to "learn" and modify their behaviour on their own. The first objective of this book is to provide the fundamental ideas that comprehensively underpin machine learning while still being simple to understand.
