Feature engineering courses can help you learn techniques for transforming raw data into meaningful features, selecting relevant variables, and creating new features to improve model performance. You can build skills in data preprocessing, handling missing values, and using domain knowledge to enhance feature sets.

Skills you'll gain: Feature Engineering, Apache Airflow, Databricks, MLOps (Machine Learning Operations), Data Pipelines, AI Orchestration, Data Lakes, Apache Spark, Data Store, AI Workflows, Data Processing, Artificial Intelligence and Machine Learning (AI/ML), Test Data, Data Transformation, Machine Learning, Data Collection, SQL, Data Storage, Machine Learning Algorithms, Warehouse Management
Advanced · Course · 1 - 4 Weeks

Microsoft
Skills you'll gain: Model Training, Feature Engineering, Model Evaluation, Model Optimization, Databricks, MLOps (Machine Learning Operations), Deep Learning, PyTorch (Machine Learning Library), Model Deployment, Redis, Embeddings, Data Science, Data Pipelines, Real Time Data, Data Analysis, Computer Science, Software Development
Advanced · Course · 1 - 3 Months

Skills you'll gain: Feature Engineering, Data Ethics, Unsupervised Learning, Dimensionality Reduction, Data Transformation, Exploratory Data Analysis, Machine Learning Methods, Machine Learning, Scikit Learn (Machine Learning Library), Natural Language Processing, Data Science, Quality Assurance, Data Pipelines, Machine Learning Algorithms, Design Thinking, Python Programming
★ 4.4 (87) · Advanced · Course · 1 - 4 Weeks

Scrimba
Skills you'll gain: Model Context Protocol, Prompt Engineering, Retrieval-Augmented Generation, LangChain, OpenAI API, Embeddings, Image Analysis, Responsible AI, AI Workflows, OpenAI, LLM Application, AI Integrations, Cloud Deployment, Memory Management, Hugging Face, Generative AI, Application Deployment, AI powered creativity, Vector Databases, Software Engineering
★ 4.5 (442) · Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Feature Engineering, Data Preprocessing, Data Cleansing, Apache Spark, Extract, Transform, Load, Data Processing, Data Pipelines, Data Transformation, Amazon Web Services, Data Quality, Data Integrity, Data Validation, Personally Identifiable Information
Intermediate · Course · 1 - 4 Weeks

Google Cloud
Skills you'll gain: Feature Engineering, Data Preprocessing, Dataflow, Tensorflow, Data Transformation, Data Store, Keras (Neural Network Library), Machine Learning Methods, Data Modeling, Machine Learning, Python Programming
★ 4.4 (37) · Intermediate · Course · 1 - 3 Months

Skills you'll gain: Prompt Engineering, Responsible AI, Multimodal Prompts, AI Workflows, Agentic systems, AI Personalization, Generative AI, Generative AI Agents, AI literacy, Context Engineering, ChatGPT, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Goal Setting, Verification And Validation
Intermediate · Course · 1 - 3 Months

Dassault Systèmes
Skills you'll gain: Computer Aided Three-Dimensional Interactive Application (CATIA), Drafting and Engineering Design, Mechanical Design, Engineering Drawings, Issue Tracking, Technical Drawing, Assembly Drawing, 3D Modeling, Computer-Aided Design, Mechanical Drawings, Collaborative Software, Document Management, Microsoft Office, Workflow Management, Innovation, Product Development, Engineering Analysis
★ 4.1 (22) · Beginner · Specialization · 1 - 3 Months

Skills you'll gain: Feature Engineering, Databricks, Data Engineering, PySpark, Data Lakes, Apache Airflow, Apache Spark, Data Pipelines, MLOps (Machine Learning Operations), Data Architecture, Data Processing, Artificial Intelligence and Machine Learning (AI/ML), Data Management, Data Storage, Python Programming, Artificial Intelligence, Machine Learning, SQL, Machine Learning Algorithms, Warehouse Management
Beginner · Specialization · 1 - 3 Months

University of Michigan
Skills you'll gain: Machine Learning Methods, Artificial Intelligence and Machine Learning (AI/ML), AI powered creativity, Precision Medicine, Generative AI, Machine Learning, AI literacy, Artificial Intelligence, Machine Learning Algorithms, Transfer Learning, Control Systems, Image Analysis, Robotics, Deep Learning, Computer Vision, Medical Imaging, Reinforcement Learning, Bioinformatics, Drug Development, Applied Machine Learning
★ 4.5 (217) · Intermediate · Specialization · 1 - 3 Months

Skills you'll gain: Prompt Engineering, Apache Spark, Large Language Modeling, Retrieval-Augmented Generation, PyTorch (Machine Learning Library), Computer Vision, Unsupervised Learning, Generative Model Architectures, Generative AI, Model Optimization, Keras (Neural Network Library), Supervised Learning, LLM Application, Generative AI Agents, Model Evaluation, Vector Databases, Fine-tuning, Machine Learning, Python Programming, Data Science
★ 4.6 (22K) · Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Prompt Engineering, Prompt Patterns, Generative AI, Exploratory Data Analysis, Data Ethics, ChatGPT, AI literacy, Feature Engineering, Predictive Modeling, Artificial Intelligence and Machine Learning (AI/ML), Responsible AI, Data Science, Data Synthesis, Machine Learning, Data Analysis
★ 4.7 (12K) · Intermediate · Specialization · 1 - 3 Months
Top-rated Feature Engineering courses offered by IBM on Coursera.
Earn a certificate in Feature Engineering from top universities and companies.
Feature engineering is the process of using domain knowledge to extract features from raw data, making it suitable for machine learning models. It plays a crucial role in improving model performance by transforming data into a format that algorithms can understand. Effective feature engineering can lead to better predictions and insights, making it a vital skill in data science and analytics.
Jobs in feature engineering typically include roles such as data scientist, machine learning engineer, and data analyst. These positions often require a strong understanding of data manipulation and modeling techniques, as well as the ability to work with large datasets. Companies across various industries seek professionals who can enhance their data-driven decision-making processes.
To excel in feature engineering, you should develop skills in data analysis, programming (especially in Python or R), and familiarity with machine learning algorithms. Understanding statistical methods and data visualization techniques is also beneficial. Additionally, knowledge of tools and libraries such as Pandas, NumPy, and Scikit-learn can enhance your capabilities in this field.
Some of the best online courses for feature engineering include Feature Engineering and AWS: Feature Engineering Data Transformation & Integrity. These courses provide practical insights and hands-on experience, helping you build a solid foundation in feature engineering.
Yes. You can start learning feature engineering on Coursera for free in two ways:
If you want to keep learning, earn a certificate in feature engineering, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.
To learn feature engineering, start by taking introductory courses that cover the basics of data science and machine learning. Engage in hands-on projects to practice your skills. Additionally, participating in online forums and communities can provide support and resources as you progress in your learning journey.
Typical topics covered in feature engineering courses include data preprocessing, feature selection, feature extraction, and techniques for handling missing data. Courses may also explore the impact of feature engineering on model performance and provide case studies to illustrate real-world applications.
For training and upskilling employees in feature engineering, consider programs like the IBM AI Engineering Professional Certificate and the DeepLearning.AI Data Engineering Professional Certificate. These professional certificates offer comprehensive training that can enhance the skills of your workforce.