Machine learning models can read data more easily and algorithm performance is improved via feature engineering.
Any data analysis might be extremely difficult and chaotic. Engineers develop new features in situations like these. A data analysis method called feature engineering makes it easier to analyse data for machine learning models.
Furthermore, a feature or variable is a numerical representation of information that is either structured or unstructured. Feature engineering is crucial to predictive modelling. Mathematical operations must be utilised. It improves the performance of machine learning programmes.
The materials listed below are some of the most exciting for learning feature engineering.
Utilizing MATLAB for Data Processing and Feature Engineering
In this course, you will build on the skills you learned in Exploratory Data Analysis using MATLAB to create the foundation required for predictive modelling. This intermediate-level course may be helpful to anyone who wants to mix data from many sources or timeframes and is interested in modelling.
People without programming experience who have some subject knowledge and exposure to computational tools can benefit from these skills. To be successful in this course, you must be familiar with basic statistics (such as histograms, averages, standard deviation, curve fitting, and interpolation) and have completed Exploratory Data Analysis using MATLAB.
Python-based Feature Engineering for Machine Learning
This is another excellent course for learning feature engineering. In this 4-hour session, you will learn the principles of feature engineering as well as how to create new features from categorical and continuous columns using the pandas package.
Additionally, addressing skewed, messy data and situations in which outliers may harm your study are covered in this course. In this course, you will also work with unstructured text data.
Machine Learning Feature Engineering
The most common and widely applied variable engineering techniques, such as mean and median imputation, one-hot encoding, logarithmic transformation, and discretization, will first be mastered in this course. Then, in order to improve the effectiveness of machine learning models, you’ll explore more intricate methods of data collection while encoding or altering variables.
You’ll learn about techniques used in finance to enhance the effectiveness of linear models, including the weight of the evidence and how to build monotonic relationships between variables and goals. Additionally, you’ll learn how to handle categorical variables with many categories and create features using date and time variables.
R’s feature engineering
The extraction of critical insights from machine learning models is facilitated by feature engineering. In order to increase the effectiveness of your model, the model-building process is iterative and calls for the development of new features using existing variables. You will examine several data sets in this course and employ feature engineering methods for both continuous and discrete variables.
PySpark’s Feature Engineering
Your responsibility is to make sense of the complicated actual world. Despite the meticulous curation and cleaning that goes into creating toy datasets like MTCars and Iris, the data still needs to be altered before powerful machine learning algorithms can extract meaning, make predictions, categorise, or cluster the data. This course will address the practical aspects of data wrangling and feature engineering, which take up 70–80% of the effort spent by data scientists. Let’s use PySpark to address this Big Data issue now that datasets are growing in size.
