Regression Machine Learning with Python

195 students enrolled

Please confirm that you want to add **Regression Machine Learning with Python** to your Wishlist.

Learn regression machine learning from basic to expert level through a practical course with Python programming language

195 students enrolled

What Will I Learn?

- Read data files and perform regression machine learning operations by installing related packages and running code on the Python IDE.
- Assess bias-variance prediction errors trade-off potentially leading to model under-fitting or over-fitting.
- Avoid model over-fitting using cross-validation for optimal parameter selection. Evaluate goodness-of-fit through coefficient of determination metric.
- Test forecasting accuracy through scale-dependent and scale-independent metrics such as mean absolute error, symmetric mean absolute percentage error and mean absolute scaled error.
- Compute generalized linear models such as linear regression and improve their prediction accuracy doing coefficient shrinkage through Ridge and Lasso regressions.
- Calculate similarity methods such as k nearest neighbors’ regression and increase their forecasting accurateness with optimal number of nearest neighbors.
- Estimate frequency methods such as decision trees regression and advance their estimation precision with ideal number of tree splits.
- Approximate ensemble methods such as random forest regression and gradient boosting machine regression to expand decision tree regression calculation exactness.
- Explore maximum margin methods such as support vector machine regression with linear and non-linear kernels and escalate their assessment exactitude with best penalty of error term.

Requirements

- Python programming language is required. Downloading instructions included.
- Python Distribution (PD) and Integrated Development Environment (IDE) are recommended. Downloading instructions included.
- Python code files provided by instructor.
- Prior basic Python programming language knowledge is useful but not required.
- Mathematical formulae kept at minimum essential level for main concepts understanding.

Description

Learn regression machine learning through a practical course with Python programming language using real world data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or make business forecasting related decisions. All of this while exploring the wisdom of best academics and practitioners in the field.

**Become a Regression Machine Learning Expert in this Practical Course with Python**

- Read data files and perform regression machine learning operations by installing related packages and running code on the Python IDE.
- Assess model bias-variance prediction errors trade-off potentially leading to under-fitting or over-fitting.
- Avoid model over-fitting using cross-validation for optimal parameter selection.
- Evaluate goodness-of-fit through coefficient of determination metric.
- Test forecasting accuracy through scale-dependent and scale-independent metrics.
- Compute generalized linear models such as linear regression, Ridge regression and Lasso regression.
- Calculate similarity methods such as optimal number of k nearest neighbors’ regression.
- Estimate frequency methods such as ideal number of splits decision trees regression.
- Approximate ensemble methods such as random forest regression and gradient boosting machine regression to enhance decision tree regression prediction accuracy.
- Explore maximum margin methods such as best penalty of error term support vector machines with linear and non-linear kernels.

**Become a Regression Machine Learning Expert and Put Your Knowledge in Practice**

Learning regression machine learning is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. And it is necessary for any business forecasting related decision.

But as learning curve can become steep as complexity grows, this course helps by leading you through step by step real world practical examples for greater effectiveness.

**Content and Overview**

This practical course contains 41 lectures and 5 hours of content. It’s designed for all regression machine learning knowledge levels and a basic understanding of Python programming language is useful but not required.

At first, you’ll learn how to read data files and perform regression machine learning computing operations by installing related packages and running code on the Python IDE. Next, you’ll asses model bias-variance prediction errors trade-off which can potentially lead to its under-fitting or over-fitting. After that, you’ll avoid model over-fitting by using cross-validation for optimal parameter selection. Later, you’ll evaluate goodness-of-fit through coefficient of determination. Then, you’ll test forecasting accuracy through scale-dependent metrics such as mean absolute error and scale-independent ones such as symmetric mean absolute percentage error and mean absolute scaled error.

After that, you’ll compute generalized linear models such as linear regression and improve its prediction accuracy through coefficient shrinkage done by Ridge regression and Lasso regression. Next, you’ll calculate similarity methods such as k nearest neighbors’ regression and increase their forecasting accurateness by selecting optimal number of nearest neighbors. Later, you’ll estimate frequency methods such as decision trees regression and advance their estimation precision with ideal number of splits.

Then, you’ll approximate ensemble methods such as random forest regression and gradient boosting machine regression in order to expand decision tree regression calculation exactness. Finally, you’ll explore maximum margin methods such as support vector machine regression using linear and non-linear or radial basis function kernels and escalate their assessment exactitude with best penalty error term.

Who is the target audience?

- Undergraduates or postgraduates at any knowledge level who want to learn about regression machine learning using Python programming language.
- Academic researchers who wish to deepen their knowledge in data mining, applied statistical learning or artificial intelligence.
- Business data scientists who desire to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.

Students Who Viewed This Course Also Viewed

Frequently Bought Together

About the Instructor