Regression Machine Learning with R هذه الدورة تدرس باللغة الإنجليزية

تفاصيل الدورة

Learn regression machine learning through a practical course with R statistical software 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 R

• Read data files and perform regression machine learning operations by installing related packages and running script code on RStudio 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 and elastic net 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.
• Analyze multi-layer perceptron methods such as optimal number of hidden nodes artificial neural network.

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 43 lectures and 5.5 hours of content. It's designed for all regression machine learning knowledge levels and a basic understanding of R statistical software 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 script code on RStudio 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 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 double coefficient shrinkage done by elastic net 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. After that, 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. Finally, you'll analyze multi-layer perceptron methods such as artificial neural network regression and advance their projecting correctness with optimal number of hidden nodes.