Machine Learning 101 : Introduction to Machine Learning
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 17 GB
Genre: eLearning Video | Duration: 25:26:54 | Language: English
Introductory Machine Learning course covering theory, algorithms and applications
What you'll learn Homepage:
The Learning Problem
Learning from Data
Is Learning Feasible?
The Linear Model
Error and Noise
Training versus Testing
Theory of Generalization
The VC Dimension
Bias-Variance Tradeoff
Neural Networks
Overfitting
Regularization
Validation
Support Vector Machines
Kernel Methods
Radial Basis Functions
Three Learning Principles
Epilogue
What is learning?
Can a machine learn?
Identify basic theoretical principles, algorithms, and applications of Machine Learning
Elaborate on the connections between theory and practice in Machine Learning
Master the mathematical and heuristic aspects of Machine Learning and their applications to real world situations
Requirements
Anyone who interest Machine Learning can take this course
Description
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 Problem
Lecture 2: Is Learning Feasible?
Lecture 3: The Linear Model I
Lecture 4: Error and Noise
Lecture 5: Training versus Testing
Lecture 6: Theory of Generalization
Lecture 7: The VC Dimension
Lecture 8: Bias-Variance Tradeoff
Lecture 9: The Linear Model II
Lecture 10: Neural Networks
Lecture 11: Overfitting
Lecture 12: Regularization
Lecture 13: Validation
Lecture 14: Support Vector Machines
Lecture 15: Kernel Methods
Lecture 16: Radial Basis Functions
Lecture 17: Three Learning Principles
Lecture 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.
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