Welcome to the world of machine learning. Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machine learning is an important component in the growing field of data science. Using statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision-making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them. In this module, you will explore some of the fundamental concepts behind machine learning. You will learn to differentiate between AI, machine, and deep learning. Further, you will also explore the importance and requirements of each process in the lifecycle of a machine learning product.

Machine learning is a hot topic, and everyone is trying to understand what it is about. With the amount of information that is out there about machine learning, you can get quickly overwhelmed. In this module, you will explore the most important topics in machine learning that you need to know. You will dive into supervised and unsupervised learning, classification, deep and reinforcement learning, as well as regression. Further, you will learn how to evaluate a machine learning model.

In this assignment, we will investigate insurance charges using a Machine Learning Regression Application, exploring how different features influence these charges. Using an interactive regression app, you will analyze how a machine learning model predicts insurance costs based on different user inputs.