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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

1. Course Promo 00:02:42
2. Introduction
1. Course Introduction 00:02:46
3. Linear Regression
1. Linear Regression 00:07:33
2. The Least Squares Method 00:11:25
3. Linear Algebra Solution to Least Squares Problem 00:12:51
4. Example Linear Regression 00:04:05
5. Summary Linear Regression 00:00:34
4. Linear Discriminant Analysis
1. Classification 00:01:15
2. Linear Discriminant Analysis 00:00:44
3. The Posterior Probability Functions 00:03:43
4. Modelling the Posterior Probability Functions 00:07:13
5. Linear Discriminant Functions 00:05:32
6. Estimating the Linear Discriminant Functions 00:06:00
7. Classifying Data Points Using Linear Discriminant Functions 00:03:09
8. LDA Example 1 00:13:52
9. LDA Example 2 00:17:38
10. Summary Linear Discriminant Analysis 00:01:34
5. Logistic Regression
1. Logistic Regression 00:01:16
2. Logistic Regression Model of the Posterior Probability Function 00:03:02
3. Estimating the Posterior Probability Function 00:08:57
4. The Multivariate Newton-Raphson Method 00:09:14
5. Maximizing the Log-Likelihood Function 00:13:52
6. Logistic Regression Example 00:09:55
7. Summary Logistic Regression 00:01:21
6. Artificial Neural Networks
1. Artificial Neural Networks 00:00:36
2. Neural Network Model of the Output Functions 00:13:00
3. Forward Propagation 00:00:51
4. Choosing Activation Functions 00:04:30
5. Estimating the Output Functions 00:02:17
6. Error Function for Regression 00:02:27
7. Error Function for Binary Classification 00:06:16
8. Error Function for Multiclass Classification 00:04:38
9. Minimizing the Error Function Using Gradient Descent 00:06:27
10. Backpropagation Equations 00:04:17
11. Summary of Backpropagation 00:01:27
12. Summary Artificial Neural Networks 00:01:48
7. Maximal Margin Classifier
1. Maximal Margin Classifier 00:02:30
2. Definitions of Separating Hyperplane and Margin 00:05:44
3. Proof 1 00:06:43
4. Maximizing the Margin 00:03:36
5. Definition of Maximal Margin Classifier 00:01:02
6. Reformulating the Optimization Problem 00:07:37
7. Proof 2 00:01:14
8. Proof 3 00:04:52
9. Proof 4 00:08:41
10. Proof 5 00:05:11
11. Solving the Convex Optimization Problem 00:01:06
12. KKT Conditions 00:01:25
13. Primal and Dual Problems 00:01:25
14. Solving the Dual Problem 00:03:31
15. The Coefficients for the Maximal Margin Hyperplane 00:00:30
16. The Support Vectors 00:00:58
17. Classifying Test Points 00:01:51
18. Maximal Margin Classifier Example 1 00:09:50
19. Maximal Margin Classifier Example 2 00:11:41
20. Summary Maximal Margin Classifier 00:00:31
8. Support Vector Classifier
1. Support Vector Classifier 00:03:54
2. Slack Variables Points on Correct Side of Hyperplane 00:03:47
3. Slack Variables Points on Wrong Side of Hyperplane 00:01:38
4. Formulating the Optimization Problem 00:03:53
5. Definition of Support Vector Classifier 00:00:44
6. A Convex Optimization Problem 00:01:47
7. Solving the Convex Optimization Problem (Soft Margin) 00:06:38
8. The Coefficients for the Soft Margin Hyperplane 00:02:09
9. Classifying Test Points (Soft Margin) 00:01:36
10. The Support Vectors (Soft Margin) 00:01:37
11. Support Vector Classifier Example 1 00:14:53
12. Support Vector Classifier Example 2 00:09:20
13. Summary Support Vector Classifier 00:00:42
9. Support Vector Machine Classifier
1. Support Vector Machine Classifier 00:01:20
2. Enlarging the Feature Space 00:05:23
3. The Kernel Trick 00:04:25
4. Summary Support Vector Machine Classifier 00:01:08