Comprehensive learning path to become a skilled Feature Engineering. Covers essential tools, frameworks, and best practices.
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Basic statistics helpful; will be taught
Some coding experience; Python or R preferred
Feature Engineering
IntermediateFeature Engineering for Machine Learning in Python
IntermediateFeature Engineering for NLP in Python
IntermediateFeature Engineering in R
IntermediateFeature Engineering with PySpark
IntermediateUsing Feature Stores for Managing Feature Engineering in Python
IntermediateApplied Machine Learning: Feature Engineering
IntermediateFeature Engineering
IntermediateFeature Engineering for Machine Learning in Python
IntermediateFeature Engineering for NLP in Python
IntermediateFeature Engineering in R
IntermediateFeature Engineering with PySpark
IntermediateUsing Feature Stores for Managing Feature Engineering in Python
IntermediateApplied Machine Learning: Feature Engineering
IntermediateFollow these courses in order to complete the learning path. Click on any course to enroll.
This course, offered by Google Cloud, delves into what constitutes a good feature and how to effectively represent it in a machine learning model. It covers essential data processing techniques for preparing a feature set, including preprocessing and feature creation, as well as feature crosses and TensorFlow Transform.
A hands-on course that covers various aspects of feature engineering for both categorical and continuous variables, as well as text data.
Learn to extract useful information from text and format it for machine learning models. The course covers POS tagging, named entity recognition, readability scores, and implementing tf-idf models using Scikit-Learn and spa Cy.
Learn various feature engineering techniques in R to develop meaningful features. The course covers changing categorical features to numerical, manipulating numeric features, and transformation techniques like Box-Cox.
This course focuses on data wrangling and feature engineering with large datasets using Py Spark. It covers preparing and cleaning data, creating new features, and building and evaluating a machine learning model.
This training focuses on managing features for machine learning models to save time and improve consistency. It teaches best practices for feature engineering and how to reuse features across projects using a feature store.
This course focuses on cleaning, normalizing, and creating features to improve the performance of machine learning models.
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Comprehensive learning path to become a skilled Feature Engineering. Covers essential tools, frameworks, and best practices.
You'll work through Understand core concepts and foundations; Build your first projects with guided tutorials; Gain confidence with hands-on exercises; Perform exploratory data analysis (EDA) on complex datasets; Clean and preprocess data for analysis; Create compelling data visualizations.
It's pitched at beginner level — a solid starting point if you're new to the topic.
7 curated courses, sequenced from foundational to advanced.
Around $155 total if you buy every course — but many include free audit options.
Enroll in this path to track your progress and stay motivated.