Feature engineering makes data reading easier for machine learning models and enhances algorithm performance.

The analysis of any data can be exceedingly chaotic and challenging. In cases like these, engineers create new features. Feature engineering, a data analysis technique, streamlines the process of analyzing data for machine learning models.

Moreover, a feature or variable is a numeric representation of structured or unstructured information. Predictive modelling relies heavily on feature engineering. The use of mathematical operations is essential. It boosts how well machine learning programmes function.

The following are some of the exciting resources to learn feature engineering.

Data Processing and Feature Engineering with MATLAB

To construct the groundwork necessary for predictive modelling, you will expand on the abilities you acquired in Exploratory Data Analysis with MATLAB in this course. Anyone who wants to combine data from several sources or times and is interested in modelling may find this intermediate-level course beneficial.

These abilities are helpful for persons without programming experience who have some subject knowledge and exposure to computational tools. You should be familiar with fundamental statistics (such as histograms, averages, standard deviation, curve fitting, and interpolation) and have finished Exploratory Data Analysis with MATLAB to succeed in this course.

Feature Engineering for Machine Learning in Python

Another good course for learning feature engineering is this one. You will learn the fundamentals of feature engineering and how to use the pandas package to generate new features from categorical and continuous columns in this 4-hour course.

Additionally, this course covers handling skewed, untidy data and circumstances in which outliers may impair your study. In addition, you will work with unstructured text data in this course.

Feature Engineering for Machine Learning

In this course, you will first master the most popular and extensively used variable engineering techniques, such as mean and median imputation, one-hot encoding, logarithmic transformation, and discretization. Then, you'll study more complex ways of capturing data while encoding or modifying variables to increase the performance of machine learning models.

You will discover approaches used in finance, such as the weight of evidence and how to construct monotonic relationships between variables and targets to improve the performance of linear models. You'll also learn how to make features out of date and time variables and deal with categorical variables with several categories.

Feature Engineering in R

Feature engineering aids in the extraction of essential insights from machine learning models. The model-building process is iterative, requiring the creation of new features using current variables to improve the efficiency of your model. In this course, you will investigate several data sets and use feature engineering techniques for continuous and discrete variables.

Feature Engineering with PySpark

The real world is messy, and your job is to make sense of it. Although toy datasets like MTCars and Iris result from careful curation and cleaning, the data still needs to be modified before robust machine learning algorithms can extract meaning, make predictions, categorize, or cluster. This course will cover the gritty elements that data scientists spend 70-80% of their time on; data wrangling and feature engineering. Now that datasets are getting bigger and bigger let's use PySpark to solve this Big Data issue.