This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, … [Read more...] about Machine Learning Introduction for Everyone
Machine Learning
Machine Learning Foundations for Product Managers
In this first course of the AI Product Management Specialization offered by Duke University's Pratt School of Engineering, you will build a foundational understanding of what machine learning is, how it works and when and why it is applied. To successfully manage an AI team or product and work collaboratively with data scientists, software engineers, and customers you need to … [Read more...] about Machine Learning Foundations for Product Managers
Google Cloud Big Data and Machine Learning Fundamentals
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud. … [Read more...] about Google Cloud Big Data and Machine Learning Fundamentals
Applied Data Science Capstone
This capstone project course will give you a taste of what data scientists go through in real life when working with real datasets. You will assume the role of a Data Scientist working for a startup intending to compete with SpaceX, and in the process follow the Data Science methodology involving data collection, data wrangling, exploratory data analysis, data visualization, … [Read more...] about Applied Data Science Capstone
Structuring Machine Learning Projects
In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched … [Read more...] about Structuring Machine Learning Projects