What is feature engineering?

Feature engineering is a process within machine learning that transforms raw data into features that a machine can recognize as part of the problem to be solved. It's a way of manually improving the observations and variables that a machine is learning based upon the data that you have. Features are tables that consist of the variables and observations that are meaningful to the problem you're trying to solve. Feature engineering consists of determining which features work best with the problem you're solving, creating the features, checking how the features work, and improving them.

Why is it important to learn feature engineering?

It's important to learn about the process of feature engineering because it can influence the result of solving a problem better than relying on algorithms alone, which means you can better solve problems based on the data that you have. When you use feature engineering properly, you can exert a positive influence on the results you achieve from the data that you're working with. To put it simply, the better features you put into the machine, the better problem solutions you'll see as a result. That's where a thorough knowledge of feature engineering comes in. The better you are at inputting features, the better you'll be at making the most of your data.

What are careers that rely on knowledge of feature engineering?

Data analysts and engineers call upon their knowledge of feature engineering in their careers. Engineers in a host of disciplines analyze, clean, and improve the data that they work with every day, and you'll find data specialists who work with feature engineering in fields as vast as healthcare, aerospace, and defense. The growing fields of mobile gaming and e-commerce also hire feature engineers to better analyze the data that they depend on.

How can online courses on Coursera help me learn feature engineering?

Whether you're looking to learn the basics of feature engineering or acquire more advanced knowledge, taking online courses on Coursera and partners like IBM and Google can help you learn the concepts and practical applications of feature engineering. You can learn about how feature engineering works in certain applications like Google Cloud or MATLAB or aim for a specialized certificate. No matter which of the feature engineering courses you choose to take, you'll have the opportunity to learn new skills that may be beneficial to your career.

Обзор
Feature Engineering

Фильтр

Результатов для запроса ''feature engineering': 237

Google Cloud
Feature Engineering
Получаемые навыки: Computational Thinking, Applied Machine Learning, Theoretical Computer Science, Beam Robotics, Python Programming, Dataflow, Cloud Computing, Data Management, Statistical Programming, Hardware Design, Machine Learning, Tensorflow, Deep Learning, Apache, Computer Architecture, Feature Engineering, Computer Programming, Google Cloud Platform
4.5
(рецензий: 1.7k)
Intermediate · Course · 1-3 Months
MathWorks
Data Processing and Feature Engineering with MATLAB
Получаемые навыки: Business Analysis, Correlation And Dependence, Data Analysis, Plot (Graphics), Data Analysis Software, Statistical Analysis, Mathematics, Statistical Visualization, Data Visualization, Feature Engineering, Matlab, Data Management, Probability & Statistics, Probability Distribution, Linear Algebra, Exploratory Data Analysis, Machine Learning
4.7
(рецензий: 307)
Intermediate · Course · 1-3 Months
IBM
AI Workflow: Feature Engineering and Bias Detection
Получаемые навыки: Data Analysis, Statistical Analysis, Algorithms, Business Analysis, Data Visualization, Statistical Visualization, Unsupervised Learning, Probability & Statistics, Machine Learning, Theoretical Computer Science, Cloud Computing, Lambda Calculus, Data Clustering Algorithms, Exploratory Data Analysis, Bias
4.4
(рецензий: 63)
Advanced · Course · 1-4 Weeks
Google
Google Data Analytics
Получаемые навыки: Algorithms, Application Development, Bias, Big Data, Budget Management, Business Analysis, Business Communication, Change Management, Cloud Computing, Communication, Computational Logic, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Cryptography, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Model, Data Security, Data Structures, Data Type, Data Visualization, Data Visualization Software, Database Administration, Database Design, Databases, Decision Making, Design and Product, Distributed Computing Architecture, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Finance, Financial Analysis, Full-Stack Web Development, General Statistics, Interactive Data Visualization, Leadership and Management, Machine Learning, Mathematical Theory & Analysis, Mathematics, Metadata, Network Security, Other Programming Languages, Plot (Graphics), Probability & Statistics, Problem Solving, Product Design, Programming Principles, Project Management, R Programming, Research and Design, SQL, Security Engineering, Security Strategy, Small Data, Software, Software Engineering, Software Security, Spreadsheet Software, Statistical Analysis, Statistical Programming, Storytelling, Strategy and Operations, Tableau Software, Theoretical Computer Science, Visual Design, Web Development
4.8
(рецензий: 70.4k)
Beginner · Professional Certificate · 3-6 Months
DeepLearning.AI, Stanford University
Machine Learning
Получаемые навыки: Accounting, Algorithms, Applied Machine Learning, Artificial Neural Networks, Calculus, Communication, Computer Programming, Computer Vision, Cost, Data Analysis, Data Management, Data Mining, Data Structures, Deep Learning, Econometrics, Feature Engineering, General Statistics, Linear Algebra, Linearity, Logistic Regression, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Operations Research, Probability & Statistics, Probability Distribution, Python Programming, Regression, Reinforcement Learning, Research and Design, Statistical Classification, Statistical Machine Learning, Statistical Programming, Strategy and Operations, Tensorflow, Theoretical Computer Science
4.9
(рецензий: 2k)
Beginner · Specialization · 1-3 Months
DeepLearning.AI
Machine Learning Engineering for Production (MLOps)
Получаемые навыки: Applied Machine Learning, Business Analysis, Change Management, Cloud Computing, Computer Networking, Computer Programming, Data Analysis, Data Management, Data Visualization, Deep Learning, DevOps, Estimation, Exploratory Data Analysis, Extract, Transform, Load, Feature Engineering, General Statistics, Leadership and Management, Machine Learning, Machine Learning Algorithms, Modeling, Network Security, Probability & Statistics, Python Programming, Security Engineering, Security Strategy, Statistical Programming, Statistical Visualization, Strategy and Operations, Tensorflow
4.7
(рецензий: 2.1k)
Advanced · Specialization · 3-6 Months
Google Cloud
Preparing for Google Cloud Certification: Machine Learning Engineer
Получаемые навыки: Algorithms, Apache, Applied Machine Learning, Bayesian Statistics, Business Analysis, Business Psychology, Cloud Computing, Cloud Platforms, Computational Thinking, Computer Architecture, Computer Networking, Computer Programming, Data Analysis, Data Management, Data Model, Data Structures, Deep Learning, DevOps, Entrepreneurship, Exploratory Data Analysis, Extract, Transform, Load, Feature Engineering, General Statistics, Google Cloud Platform, Hardware Design, Kubernetes, Machine Learning, Mathematical Optimization, Network Security, Performance Management, Probability & Statistics, Python Programming, Regression, Security Engineering, Security Strategy, Statistical Programming, Strategy and Operations, Tensorflow, Theoretical Computer Science
4.6
(рецензий: 24.2k)
Intermediate · Professional Certificate · 3-6 Months
IBM Skills Network
IBM Machine Learning
Получаемые навыки: Algebra, Algorithms, Analysis, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Business Analysis, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Data Analysis, Data Management, Data Structures, Data Visualization, Databases, Deep Learning, Dimensionality Reduction, Experiment, Exploratory Data Analysis, Feature Engineering, Forecasting, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematics, NoSQL, Probability & Statistics, Python Programming, Regression, Reinforcement Learning, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Theoretical Computer Science
4.6
(рецензий: 1k)
Intermediate · Professional Certificate · 3-6 Months

Результаты поиска, связанного с feature engineering

feature engineering en français

feature engineering 日本語版

feature engineering em português brasileiro

feature engineering en español

ai workflow: feature engineering and bias detection

data processing and feature engineering with matlab

10 самых популярных курсов по запросу feature engineering

Feature Engineering: Google Cloud
Data Processing and Feature Engineering with MATLAB: MathWorks
AI Workflow: Feature Engineering and Bias Detection: IBM
Feature Engineering en Español: Google Cloud
Feature Engineering 日本語版: Google Cloud
Feature Engineering em Português Brasileiro: Google Cloud
Feature Engineering en Français: Google Cloud
Google Data Analytics: Google
Machine Learning: DeepLearning.AI
Machine Learning Engineering for Production (MLOps): DeepLearning.AI

Навыки, которые вы можете получить здесь: Machine Learning

Программирование На Языке Python (33)

TensorFlow (32)

Глубокое Обучение (30)

Искусственная Нейронная Сеть (24)

Большие Данные (18)

Статистическая Классификация (17)

Обучения С Подкреплением (13)

Алгебра (10)

Байесовский (10)

Линейная Алгебра (10)

Линейная Регрессия (9)

NumPy (9)

Часто задаваемые вопросы на тему Feature Engineering

Feature engineering is a process within machine learning that transforms raw data into features that a machine can recognize as part of the problem to be solved. It's a way of manually improving the observations and variables that a machine is learning based upon the data that you have. Features are tables that consist of the variables and observations that are meaningful to the problem you're trying to solve. Feature engineering consists of determining which features work best with the problem you're solving, creating the features, checking how the features work, and improving them.‎
It's important to learn about the process of feature engineering because it can influence the result of solving a problem better than relying on algorithms alone, which means you can better solve problems based on the data that you have. When you use feature engineering properly, you can exert a positive influence on the results you achieve from the data that you're working with. To put it simply, the better features you put into the machine, the better problem solutions you'll see as a result. That's where a thorough knowledge of feature engineering comes in. The better you are at inputting features, the better you'll be at making the most of your data.‎
Data analysts and engineers call upon their knowledge of feature engineering in their careers. Engineers in a host of disciplines analyze, clean, and improve the data that they work with every day, and you'll find data specialists who work with feature engineering in fields as vast as healthcare, aerospace, and defense. The growing fields of mobile gaming and e-commerce also hire feature engineers to better analyze the data that they depend on.‎
Whether you're looking to learn the basics of feature engineering or acquire more advanced knowledge, taking online courses on Coursera and partners like IBM and Google can help you learn the concepts and practical applications of feature engineering. You can learn about how feature engineering works in certain applications like Google Cloud or MATLAB or aim for a specialized certificate. No matter which of the feature engineering courses you choose to take, you'll have the opportunity to learn new skills that may be beneficial to your career.‎

Часто задаваемые вопросы предоставляются в ознакомительных целях. Учащимся рекомендуется дополнительно убедиться в том, что интересующие их курсы и другие материалы соответствуют их личным, профессиональным и финансовым потребностям.