Predictive Analytics using R 3.5 [Video]

Predictive Analytics using R 3.5 [Video]

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 08m | 576 MB
eLearning | Skill level: All Levels

Predictive Analytics using R 3.5 [Video]: Explore advanced techniques and algorithms for predictive modeling to gain insights from your data

Predictive modeling uses statistics to predict outcomes of events. It can be applied to any type of unknown event, regardless of when it occurred. This course will introduce you to the most widely used predictive modeling techniques and their core principles.

This course will help you to perform key predictive analytics tasks, such as training and testing predictive models for classification and regression tasks, and scoring new data sets. The course covers the most common data mining tools, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also describes visualization techniques using core tools to visualize patterns in data organized into groups.

• Apply various techniques and modeling algorithms by using R for ML tasks
• Estimate and measure the performance of models used for regression and classification problems
• Implement spot-checking methods for linear and nonlinear algorithms
• Compare and select trained models and summarize the results with plotting techniques
• Tune and apply machine learning algorithm hyperparameters with different methods
• Summarize your data using descriptive statistics

By the end of the course, you will be able to design your own machine learning predictive models using R 3.5.