Advanced Handling of Many Feature Situations ★★★★ Master Level
Become a problem solver and find solutions to business-relevant situations with many features. In this two-day training, we outline methods to handle challenges, whether it be large quantities of data or modeling conditions with many variables. Familiarizing you with problems of many feature situations so you are prepared for any task.
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Advanced Handling of Many Feature Situations
About the courseIn business applications where the information in the data is spread over many features, problems arise: either very poor model performance or too little computing power. In this course, we teach you methods to build a model with good model validation scores and fast performance. This course will prepare you to tackle any situation through strategic methods for reducing data dimensionality, validation metrics for outcomes, and business applications of many feature situations. With the aid of our expert trainers, you will apply these skills to real and common cases to realize business opportunities with your own applications.
Why this is for youHave you ever been stumped by the sheer volume of information and been left not knowing where to start? Or discovered that the number of variables has made it impossible to perform a good explorative analysis before you even start modeling? This module has been designed to show you the different techniques to solve these issues and ensure you can handle even the largest data sets imaginable.
For whomThis is an advanced level training for Data Scientists who have completed badge Advanced Model Optimization (3330). It involves tough coding challenges and various abstract topics which means you need to be advanced in Python and modeling principles.
What you’ll learn
- The problems of many feature situations and typical cases when they arise
- Methods for reducing data dimensionality
- How to apply several techniques in a step-by-step approach
- An overview and calculation of validation metrics
- How to interpret outcomes of reduction methods
- Business outcomes
- Understanding problems with many feature situations – Being able to explain what issues there are when working with many features
- Methods for reducing data dimensionality – Proficient in choosing the right method for dimensionality reduction and explaining this
- Applying data dimensionality reduction techniques – Proficient in applying dimensionality reduction methods to large data sets
- Validation of dimensionality reduction methods – Knowing how to score and validate outcomes of your dimensionality reduction
- Business applications of many feature situations – Spotting business opportunities for many feature situations