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Anyone who interest Machine Learning can take this courseCourse Duration: 13.5 Hours
Course Instructor: Didaktik ACADEMY
Introduction to Machine Learning
Machine Learning 101 : Introduction to Machine Learning
Introductory Machine Learning course covering theory, algorithms and applications.
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion:
What is learning?Can a machine learn?How to do it?How to do it well?Take-home lessons.
Outline of this Course;
Lecture 1: The Learning ProblemLecture 2: Is Learning Feasible?Lecture 3: The Linear Model ILecture 4: Error and NoiseLecture 5: Training versus TestingLecture 6: Theory of GeneralizationLecture 7: The VC DimensionLecture 8: Bias-Variance TradeoffLecture 9: The Linear Model IILecture 10: Neural NetworksLecture 11: OverfittingLecture 12: RegularizationLecture 13: ValidationLecture 14: Support Vector MachinesLecture 15: Kernel MethodsLecture 16: Radial Basis FunctionsLecture 17: Three Learning PrinciplesLecture 18: Epilogue
This course has some videos on youtube that has Creative Commen Licence (CC).
Who this course is for:
If you have no prior coding or scripting experience, you can also attend this lesson.
Anyone who interest Data Science
Anyone who interest Learning From Data
Anyone who interest how deep learning really works
Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.