We have introduced ML with its categories and applications. It is a rapidly growing field of study having numerous applications in building intelligent systems. We have categorized ML into supervised, unsupervised, and reinforcement learning algorithms. Each of the categories have their applications in solving tasks such as classification, clustering, regression, and machine translation.
We have implemented a simple learning algorithm that defines a calculation function based on experiences provided as an input. We called it a dataset that we used to train the system. Training with datasets (called experiences) is one of the key properties in ML systems.
Finally, we introduced and discussed ANNs applied to recognize patterns. ML and neural networks go hand in hand in solving tasks. The chapter provides you with the necessary introduction to the field along with several...
