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Fundamentals Of Machine Learning by Yiqiao Yin
Last updated 6/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.91 GB | Duration: 8h 39m
This course will start your career in data science.
Last updated 6/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.91 GB | Duration: 8h 39m
This course will start your career in data science.
What you'll learn
Learn about the fundamental principles of machine learning
Build customized models to use for different data science projects
Build customized Deep Learning models to start your own data science career
Start your data science career and connect with the tutor in industry
Requirements
No prior mathematical or programming knowledge required. Some python programming experience is helpful.
Description
This is an introduction course of machine learning. The course will cover a wide range of topics to teach you step by step from handling a dataset to model delivery. The course assumes no prior knowledge of the students. However, some prior training in python programming and some basic calculus knowledge is definitely helpful for the course. The expectation is to provide you the same knowledge and training as that is provided in an intro Machine Learning or Artificial Intelligence course at a credited undergraduate university computer science program. The course is comparable to the Introduction of Statistical Learning, which is the intro course to machine learning written by none other than the greatest of all: Trevor Hastie and Rob Tibshirani! The course was modeled from the "Introduction to Statistical Learning" from Stanford University.The course is taught by Yiqiao Yin, and the course materials are provided by a team of amazing instructors with 5+ years of industry experience. All instructors come from Ivy League background and everyone is eager to share with you what they know about the industry. The course has the following topics:IntroductionBasics in Statistical LearningLinear RegressionClasificationSampling and BootstrapModel Selection & RegularizationGoing Beyond LinearityTree-based MethodSupport Vector MachineDeep LearningUnsupervised LearningClassification MetricsThe course is composed of 3 sections:Lecture series
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