Machine Learning Introduction for Everyone

IBM via Coursera

Coursera based on 272 ratings

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Overview

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. Youll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. Youll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more. Our labs give you hands-on experience with these machine learning and data science concepts. You will develop concrete machine learning skills as well...

Syllabus

Machine Learning for Everyone

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 Topics

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.