Course: Regression Math
duration: 6 hours |
Language: English (US) |
access duration: 90 days |
Details
This course will teach you the basics of regression math. You will start by exploring linear regressions, a power technique for quantifying cause and effect relationships. Explore the process of fitting a straight line through a set of points. And learn how to define residuals, use the least square error and measure R-squared. Next, you will move on to gradient descents and logistic regressions. This is an extremely powerful numerical optimization technique widely used to find optimal values of model parameters during the model training phase of machine learning.
Result
After completing this course, you'll be able to identify the need for linear regression and implement it effectively. And, you'll be able to implement logistic regression using gradient descent.
Prerequisites
No prerequisites.
Target audience
Software Developer, Web Developer
Content
Regression Math
Regression Math: Getting Started with Linear Regression
- Linear Regression analysis is a simple yet powerful technique for quantifying cause and effect relationships. Use this course to get your head around linear regression as the process of fitting a straight line through a set of points. Learn how to define residuals and use the least square error.
- Define and measure the R-squared, implement regression analysis, visualize your data by computing a correlation matrix and plotting it in the form of a correlation heatmap, and use scatter plots as a prelude to performing the regression analysis.
- Finish by implementing the regression analysis first using functions that you write yourself and then using the scikit-learn python library.
- By the end of the course, you'll be able to identify the need for linear regression and implement it effectively.
Regression Math: Using Gradient Descent & Logistic Regression
- Gradient descent is an extremely powerful numerical optimization technique widely used to find optimal values of model parameters during the model training phase of machine learning.
- Use this course as an introduction to gradient descent, examining how it can be used in a wide variety of optimization problems. Explore how it can be used to perform linear regression, carefully studying the matrix equations used to compute the gradients and updating the model parameters using the gradients as well as the learning rate hyperparameter.
- Finally, apply a form of gradient descent known as stochastic gradient descent to fit an S-curve, thus implementing logistic regression on a data set.
- By the end of the course, you'll be able to assuredly implement logistic regression using gradient descent.
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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