[Stone River eLearning] Math for Machine Learning - [FCO]

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[FreeCoursesOnline.Me] [Stone River eLearning] Math for Machine Learning - [FCO]
  • 01.Course Promo.mp4 (4.7 MB)
  • 02.Course Introduction.mp4 (5.2 MB)
  • 03.Linear Regression.mp4 (11.0 MB)
  • 04.The Least Squares Method.mp4 (17.5 MB)
  • 05.Linear Algebra Solution to Least Squares Problem.mp4 (17.9 MB)
  • 06.Example Linear Regression.mp4 (6.0 MB)
  • 07.Summary Linear Regression.mp4 (1.6 MB)
  • 08.Classification.mp4 (1.7 MB)
  • 09.Linear Discriminant Analysis.mp4 (967.1 KB)
  • 10.The Posterior Probability Functions.mp4 (5.3 MB)
  • 11.Modelling the Posterior Probability Functions.mp4 (11.3 MB)
  • 12.Linear Discriminant Functions.mp4 (8.4 MB)
  • 13.Estimating the Linear Discriminant Functions.mp4 (8.6 MB)
  • 14.Classifying Data Points Using Linear Discriminant Functions.mp4 (4.9 MB)
  • 15.LDA Example 1.mp4 (20.2 MB)
  • 16.LDA Example 2.mp4 (26.9 MB)
  • 17.Summary Linear Discriminant Analysis.mp4 (4.8 MB)
  • 18.Logistic Regression.mp4 (1.6 MB)
  • 19.Logistic Regression Model of the Posterior Probability Function.mp4 (4.2 MB)
  • 20.Estimating the Posterior Probability Function.mp4 (12.9 MB)
  • 21.The Multivariate Newton-Raphson Method.mp4 (16.6 MB)
  • 22.Maximizing the Log-Likelihood Function.mp4 (21.4 MB)
  • 23.Logistic Regression Example.mp4 (14.3 MB)
  • 24.Summary Logistic Regression.mp4 (3.7 MB)
  • 25.Artificial Neural Networks.mp4 (778.6 KB)
  • 26.Neural Network Model of the Output Functions.mp4 (18.8 MB)
  • 27.Forward Propagation.mp4 (1.6 MB)
  • 28.Choosing Activation Functions.mp4 (5.9 MB)
  • 29.Estimating the Output Functions.mp4 (3.0 MB)
  • 30.Error Function for Regression.mp4 (3.3 MB)
  • 31.Error Function for Binary Classification.mp4 (8.1 MB)
  • 32.Error Function for Multiclass Classification.mp4 (6.3 MB)
  • 33.Minimizing the Error Function Using Gradient Descent.mp4 (9.2 MB)
  • 34.Backpropagation Equations.mp4 (6.1 MB)
  • 35.Summary of Backpropagation.mp4 (2.3 MB)
  • 36.Summary Artificial Neural Networks.mp4 (5.0 MB)
  • 37.Maximal Margin Classifier.mp4 (3.1 MB)
  • 38.Definitions of Separating Hyperplane and Margin.mp4 (8.4 MB)
  • 39.Proof 1.mp4 (10.8 MB)
  • 40.Maximizing the Margin.mp4 (5.3 MB)
  • 41.Definition of Maximal Margin Classifier.mp4 (1.5 MB)
  • 42.Reformulating the Optimization Problem.mp4 (12.2 MB)
  • 43.Proof 2.mp4 (1.8 MB)
  • 44.Proof 3.mp4 (7.3 MB)
  • 45.Proof 4.mp4 (13.0 MB)
  • 46.Proof 5.mp4 (8.1 MB)
  • 47.Solving the Convex Optimization Problem.mp4 (1.7 MB)
  • 48.KKT Conditions.mp4 (2.7 MB)
  • 49.Primal and Dual Problems.mp4 (2.1 MB)
  • 50.Solving the Dual Problem.mp4 (4.8 MB)
  • 51.The Coefficients for the Maximal Margin Hyperplane.mp4 (677.0 KB)
  • 52.The Support Vectors.mp4 (1.3 MB)
  • 53.Classifying Test Points.mp4 (2.5 MB)
  • 54.Maximal Margin Classifier Example 1.mp4 (14.4 MB)
  • 55.Maximal Margin Classifier Example 2.mp4 (16.7 MB)
  • 56.Summary Maximal Margin Classifier.mp4 (1.6 MB)
  • 57.Support Vector Classifier.mp4 (5.3 MB)
  • 58.Slack Variables Points on Correct Side of Hyperplane.mp4 (5.5 MB)
  • 59.Slack Variables Points on Wrong Side of Hyperplane.mp4 (2.2 MB)
  • 60.Formulating the Optimization Problem.mp4 (5.5 MB)
  • 61.Definition of Support Vector Classifier.mp4 (1.2 MB)
  • 62.A Convex Optimization Problem.mp4 (3.3 MB)
  • 63.Solving the Convex Optimization Problem (Soft Margin).mp4 (9.4 MB)
  • 64.The Coefficients for the Soft Margin Hyperplane.mp4 (2.9 MB)
  • 65.Classifying Test Points (Soft Margin).mp4 (2.4 MB)
  • 66.The Support Vectors (Soft Margin).mp4 (2.3 MB)
  • 67.Support Vector Classifier Example 1.mp4 (22.1 MB)
  • 68.Support Vector Classifier Example 2.mp4 (14.3 MB)
  • 69.Summary Support Vector Classifier.mp4 (1.9 MB)
  • 70.Support Vector Machine Classifier.mp4 (1.7 MB)
  • 71.Enlarging the Feature Space.mp4 (8.2 MB)
  • 72.The Kernel Trick.mp4 (6.9 MB)
  • 73.Summary Support Vector Machine Classifier.mp4 (3.4 MB)
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Description



Author : Richard Han
Publisher[b/]: Stone River eLearning
[b]Release Date : May 2018
ISBN : 200000006A0200
Language : English
Torrent Contains : 79 Files
Course Source : https://www.oreilly.com/library/view/math-for-machine/200000006A0200/

Video Description

Would you like to learn a mathematics subject that is crucial for many high-demand lucrative career fields such as: Computer Science Data Science Artificial Intelligence If you're looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you. Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. You intend to pursue a masters degree or PhD, and machine learning is a required or recommended subject. Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have created many successful online math courses that students around the world have found invaluable—courses in linear algebra, discrete math, and calculus.

Table of Contents

• Course Promo
• Introduction
• Linear Regression
• Linear Discriminant Analysis
• Logistic Regression
• Artificial Neural Networks
• Maximal Margin Classifier
• Support Vector Classifier
• Support Vector Machine Classifier.

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[Stone River eLearning] Math for Machine Learning - [FCO]

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521.3 MB
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[Stone River eLearning] Math for Machine Learning - [FCO]

Torrent hash: 049F9792733CA74149FBD439DEDBEFCD218ABBAD