About This Course

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 master’s 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.

In this course, I cover the core concepts such as:

  • Linear Regression
  • Linear Discriminant Analysis
  • Logistic Regression
  • Artificial Neural Networks
  • Support Vector Machines

After taking this course, you will feel carefree and confident. I will break it all down into bite-sized no-brainer chunks. I explain each definition and go through each example step by step so that you understand each topic clearly. Practice problems are provided for you, and detailed solutions are also provided to check your understanding.

Our Promise to You

By the end of this course, you will have learned about mathematical concepts that are used in data science, computer science and artificial intelligence.

30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about math for machine learning.

Course Curriculum

Section 1 - Introduction
Introduction Lecture 00:00:00
Section 2 - Linear Regression
Linear Regression 00:00:00
The Least Squares Method 00:00:00
Linear Algebra Solution To Least Squares Problem 00:00:00
Example: Linear Regression 00:00:00
Summary: Linear Regression 00:00:00
Problem Set: Linear Regression 00:00:00
Solution Set: Linear Regression 00:00:00
Section 3 - Linear Discriminant Analysis
Classification 00:00:00
Linear Discriminant Analysis 00:00:00
The Posterior Probability Functions 00:00:00
Modelling The Posterior Probability Functions 00:00:00
Linear Discriminant Functions 00:00:00
Estimating The Linear Discriminant Functions 00:00:00
Classifying Data Points Using Linear Discriminant Functions 00:00:00
LDA Example 1 00:00:00
LDA Example 2 00:00:00
Summary: Linear Discriminant Analysis 00:00:00
Problem Set: Linear Discriminant Analysis 00:00:00
Solution Set: Linear Discriminant Analysis 00:00:00
Section 4 - Logistic Regression
Logistic Regression 00:00:00
Logistic Regression Model Of The Posterior Probability Function 00:00:00
Estimating The Posterior Probability Function 00:00:00
The Multivariate Newton-Raphson Method 00:00:00
Maximizing The Log-Likelihood Function 00:00:00
Example: Logistic Regression 00:00:00
Summary: Logistic Regression 00:00:00
Problem Set: Logistic Regression 00:00:00
Solution Set: Logistic Regression 00:00:00
Section 5 - Artificial Neural Networks
Artificial Neural Networks 00:00:00
Neural Network Model Of The Output Functions 00:00:00
Forward Propagation 00:00:00
Choosing Activation Functions 00:00:00
Estimating The Output Functions 00:00:00
Error Function For Regression 00:00:00
Error Function For Binary Classification 00:00:00
Error Function For Multi-class Classification 00:00:00
Minimizing The Error Function Using Gradient Descent 00:00:00
Backpropagation Equations 00:00:00
Summary Of Backpropagation 00:00:00
Summary: Artificial Neural Networks 00:00:00
Problem Set: Artificial Neural Networks 00:00:00
Solution Set: Artificial Neural Networks 00:00:00
Section 6 - Maximal Margin Classifier
Maximal Margin Classifier 00:00:00
Definitions Of Separating Hyperplane And Margin 00:00:00
Maximizing the Margin 00:00:00
Definition Of Maximal Margin Classifier 00:00:00
Reformulating The Optimization Problem 00:00:00
Solving The Convex Optimization Problem 00:00:00
KKT Conditions 00:00:00
Primal And Dual Problems 00:00:00
Solving The Dual Problem 00:00:00
The Coefficients For The Maximal Margin Hyperplane 00:00:00
The Support Vectors 00:00:00
Classifying Test Points 00:00:00
Maximal Margin Classifier Example 1 00:00:00
Maximal Margin Classifier Example 2 00:00:00
Summary: Maximal Margin Classifier 00:00:00
Problem Set: Maximal Margin Classifier 00:00:00
Solution Set: Maximal Margin Classifier 00:00:00
Section 7 - Support Vector Classifier
Support Vector Classifier 00:00:00
Slack Variables: Points On Correct Side Of Hyperplane 00:00:00
Slack Variables: Points On Wrong Side Of Hyperplane 00:00:00
Formulating The Optimization Problem 00:00:00
Definition Of Support Vector Classifier 00:00:00
A Convex Optimization Problem 00:00:00
Solving The Convex Optimization Problem (Soft Margin) 00:00:00
The Coefficients For The Soft Margin Hyperplane 00:00:00
The Support Vectors (Soft Margin) 00:00:00
Classifying Test Points (Soft Margin) 00:00:00
Support Vector Classifier Example 1 00:00:00
Support Vector Classifier Example 2 00:00:00
Summary: Support Vector Classifier 00:00:00
Problem Set: Support Vector Classifier 00:00:00
Solution Set: Support Vector Classifier 00:00:00
Section 8 - Support Vector Machine Classifier
Support Vector Machine Classifier 00:00:00
Enlarging The Feature Space 00:00:00
The Kernel Trick 00:00:00
Summary: Support Vector Machine Classifier 00:00:00
Section 9 - Conclusion
Concluding Letter 00:00:00
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