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This video is part of an online course, How Google does Machine Learning from Google Cloud. Enroll today at https://www.coursera.org/learn/google-machine-learning?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=googlecloud to get access to the full course. About this course: What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this. Visit https://www.coursera.org/learn/google-machine-learning?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=googlecloud to learn more! Specialization: https://www.coursera.org/specializations/machine-learning-tensorflow-gcp?utm_source=yt&utm_medium=social&utm_campaign=channel&utm_content=googlecloud Keep in touch with Coursera! Twitter: https://twitter.com/coursera Facebook: https://www.facebook.com/Coursera/ ------ Coursera partners with more than 275 leading universities and companies to bring flexible, affordable, job-relevant online learning to individuals and organizations worldwide. We offer a range of learning opportunities—from hands-on projects and courses to job-ready certificates and degree programs. Remember to like, subscribe, and share this video with friends and colleagues looking to create new career possibilities. Visit Coursera at: https://bit.ly/46X30CH Unlock unlimited learning with a Coursera Plus subscription: https://bit.ly/48gBUHW Check out more from Coursera: @Coursera Connect with us: facebook.com/coursera twitter.com/coursera instagram.com/coursera tiktok.com/@coursera #CourseraPlus #Coursera #LearnWithoutLimits #OnlineLearning
