Summary
In this chapter, we introduced ML, alongside its categories and applications. It is a rapidly growing field of study that has numerous applications in building intelligent systems. We categorized ML into supervised, unsupervised, and reinforcement learning algorithms. Each of these categories has its applications in solving tasks, such as classification, clustering, regression, and machine translation.
Then, we implemented a simple learning algorithm that defines a calculation function based on experiences provided as input. We called this a dataset and used it to train the system. Training with datasets (called experiences) is one of the key properties of ML systems.
Finally, we introduced and discussed ANNs and applied them to recognize patterns. ML and neural networks go hand in hand in solving tasks. This chapter provided you with a necessary introduction to this field, along with several examples of tasks, so that you can spend some time diving into the topic. This...