$129.99
Enroll Now 5 Add to Cart
Certification

Industry recognized certification enables you to add this credential to your resume upon completion of all courses

Need Custom Training for Your Team?
Get Quote
Call Us

Toll Free (844) 397-3739

Inquire About This Course
Instructor
Dr. Stephen Huff, Instructor - Supervised Learning: Linear Regression

Dr. Stephen Huff

Is currently employed as a consulting scientist, advising DHS Science and Technology programs regarding machine learning technology. He earned his Ph.D. (Bioinformatics) from the University of Houston. In addition to that, he has 3 Masters and a B.S.: an M.S. (Joint Professional Military Education) from Air Command and Staff College, M.S. (Management Information Systems) from Wright State University, M.S. (Biology) from University of Houston at Clear Lake, and a B.S. (Microbiology with Chemistry Minor) from University of Texas at Arlington.
Read More
Show Less

Instructor: Dr. Stephen Huff

    • Acquire improved ability to discriminate, differentiate, and conceptualize appropriate methods of supervised machine learning methods.
    • Instructor has a Ph.D. in Bioinformatics and works as a consultant for the DHS Agency.

Course Description

Read More

What am I going to get from this course?

  • Improved ability to discriminate, differentiate, and conceptualize appropriate methods of supervised machine learning methods
  • Improved general awareness regarding use of these models as L1-/L2-norm regularizers, loss-functions, and more.  

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Basic understanding of machine learning principles
  • Basic awareness of linear regression methods
  • Basic general understanding of information technology principles
  • Optional prerequisites: Basic working knowledge of Python and Keras, additional awareness of at least one Keras backend (TensorFlow, Microsoft Cognitive, ToolKit and/or Theano)

Who should take this course? Who should not?

  • Information Technology professionals with basic knowledge of machine learning theory and linear regression

Curriculum

Module 1: Course Introduction

Lecture 1 Promo
Lecture 2 Course Introduction
Lecture 3 Course Overview
Lecture 4 Bias Error
Lecture 5 Lessons Summary

Module 2: Ordinary Least Squares Linear Regression

Lecture 6 Lesson Overview
Lecture 7 Ordinary Least Squares

Module 3: Ridge Regression

Lecture 8 Lesson Overview
Lecture 9 Ridge Trace
Lecture 10 LASSO Regression
Lecture 11 Use Cases in Security

Module 4: Elastic Net Regression

Lecture 12 Lesson Overview
Lecture 13 Use Cases in Temporal Analyses

Module 5: Non-Parametric Kernel-Based Regression

Lecture 14 Lesson Overview
Lecture 15 Kernel Regression
Lecture 16 Gaussian Process

Module 6: Application in Healthcare, Education, Financial Services, Retail, & Travel Enterprise

Lecture 17 Lesson Overview & Various Applications in Different Industries

Module 7: Code Base

Lecture 18 Lesson Overview
Lecture 19 Activation Functions in Keras
Lecture 20 Course Conclusion