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  • Feature Engineering

Results for "feature engineering"


  • G

    Google Cloud

    Feature Engineering

    Skills you'll gain: Feature Engineering, Data Preprocessing, Model Optimization, Dataflow, Tensorflow, Data Pipelines, Keras (Neural Network Library), Data Processing, Data Transformation, Applied Machine Learning, Data Modeling, Data Store, Real Time Data, Machine Learning

    ★ 4.5 (1.8K) · Intermediate · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • I

    IBM

    AI Workflow: Feature Engineering and Bias Detection

    Skills you'll gain: Feature Engineering, Data Ethics, Unsupervised Learning, Dimensionality Reduction, Responsible AI, Text Mining, Data Preprocessing, Data Transformation, Anomaly Detection, Exploratory Data Analysis, Machine Learning, Scikit Learn (Machine Learning Library), Natural Language Processing, Data Science, Quality Assurance, Data Pipelines, Machine Learning Algorithms, Classification Algorithms, Design Thinking, Python Programming

    ★ 4.4 (80) · Advanced · Course · 1 - 4 Weeks

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • S

    Simplilearn

    DevOps & Site Reliability Engineering Mastery Certification

    Skills you'll gain: Terraform, Git (Version Control System), Containerization, Incident Management, Site Reliability Engineering, Infrastructure as Code (IaC), Cloud-Native Computing, Version Control, Prometheus (Software), Grafana, Kubernetes, DevSecOps, Devops Tools, CI/CD, DevOps, Docker (Software), Microservices, Incident Response, Cloud Infrastructure, Multi-Cloud

    Beginner · Specialization · 3 - 6 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • C

    Coursera

    Embedded Firmware Engineering

    Skills you'll gain: Embedded Software, Real-Time Operating Systems, Embedded Systems, Software Architecture, Code Reusability, Pseudocode, Microarchitecture, Memory Management, Systems Architecture, Computer Hardware, System Programming, Computer Architecture, Software Design, Cross Platform Development, Software Systems, Hardware Architecture, Systems Design, Software Design Patterns, Internet Of Things, Hardware Troubleshooting

    Intermediate · Specialization · 1 - 3 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • C

    Coursera

    Deep Learning Engineering

    Skills you'll gain: Model Deployment, Fine-tuning, PyTorch (Machine Learning Library), Model Evaluation, Model Training, Vision Transformer (ViT), Model Optimization, Transfer Learning, MLOps (Machine Learning Operations), Natural Language Processing, Debugging, Containerization, Kubernetes, Docker (Software), Distributed Computing, Performance Tuning, Tensorflow, Deep Learning, Cloud Computing, Data Pipelines

    Advanced · Specialization · 1 - 3 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • W

    Whizlabs

    AWS: Feature Engineering Data Transformation & Integrity

    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 Wrangling, Responsible AI, Data Quality, Data Integrity, Data Validation, Model Training, Personally Identifiable Information

    Intermediate · Course · 1 - 4 Weeks

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered

What brings you to Coursera today?

  • G

    Google Cloud

    Feature Engineering en Español

    Skills you'll gain: Feature Engineering, Data Preprocessing, Dataflow, Tensorflow, Data Pipelines, Data Transformation, Data Processing, Model Deployment, Keras (Neural Network Library), Google Cloud Platform, Machine Learning, Python Programming, Statistical Methods

    ★ 4.4 (37) · Intermediate · Course · 1 - 3 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • I

    IBM

    IBM AI Engineering

    Skills you'll gain: Prompt Engineering, Apache Spark, PyTorch (Machine Learning Library), Large Language Modeling, Retrieval-Augmented Generation, Transfer Learning, Model Evaluation, Computer Vision, Unsupervised Learning, Generative Model Architectures, Generative AI, PySpark, Prompt Engineering Tools, Vision Transformer (ViT), Keras (Neural Network Library), Vector Databases, Fine-tuning, Machine Learning, Python Programming, Data Science

    ★ 4.6 (22K) · Intermediate · Professional Certificate · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Build toward a degree
    Build toward a degree
  • C

    Coursera

    LLM Engineering That Works: Prompting, Tuning, and Retrieval

    Skills you'll gain: Responsible AI, MLOps (Machine Learning Operations), Model Optimization, Model Deployment, Feature Engineering, Product Requirements, Prompt Engineering, Data Ethics, Prompt Patterns, LLM Application, Model Training, Kubernetes, AI Security, Systems Architecture, Large Language Modeling, Model Evaluation, PyTorch (Machine Learning Library), Apache Airflow, Data Pipelines, SQL

    Intermediate · Professional Certificate · 3 - 6 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • S

    Scrimba

    AI Engineering

    Skills you'll gain: Model Context Protocol, Prompt Engineering, Retrieval-Augmented Generation, LangChain, OpenAI API, Prompt Patterns, Embeddings, Image Analysis, Responsible AI, AI Workflows, OpenAI, LLM Application, AI Integrations, Cloud Deployment, Cloud Applications, Multimodal Prompts, Memory Management, Hugging Face, Vector Databases, Software Engineering

    ★ 4.5 (347) · Intermediate · Specialization · 3 - 6 Months

    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • P

    Packt

    AI Engineering Masterclass: From Zero to AI Hero

    Skills you'll gain: Recurrent Neural Networks (RNNs), Model Evaluation, Supervised Learning, Feature Engineering, Probability & Statistics, Transfer Learning, NumPy, Matplotlib, Model Training, Model Optimization, Convolutional Neural Networks, Deep Learning, Applied Machine Learning, Statistics, Data Visualization, Keras (Neural Network Library), Statistical Hypothesis Testing, Fine-tuning, Machine Learning Methods, Python Programming

    Intermediate · Specialization · 3 - 6 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
  • M

    MathWorks

    Electrified Systems Design Engineer

    Skills you'll gain: Power Electronics, Simulation and Simulation Software, Model Based Systems Engineering, Electronic Systems, Electrical Systems, Electrical Engineering, Three-Phase, Machine Controls, Systems Integration, Engineering Practices, Simulations, Electrical Power, Electronics Engineering, Electronics, Electric Power Systems, Engineering Analysis, Engineering, Engineering Design Process, Performance Tuning, Environmental Issue

    ★ 4.8 (77) · Beginner · Professional Certificate · 3 - 6 Months

    Category: New
    New
    Status: Free Trial
    Free Trial
    Category: Credit offered
    Credit offered
1234…440

In summary, here are 10 of our most popular feature engineering courses

  • Feature Engineering: Google Cloud
  • AI Workflow: Feature Engineering and Bias Detection: IBM
  • DevOps & Site Reliability Engineering Mastery Certification: Simplilearn
  • Embedded Firmware Engineering: Coursera
  • Deep Learning Engineering: Coursera
  • AWS: Feature Engineering Data Transformation & Integrity : Whizlabs
  • Feature Engineering en Español: Google Cloud
  • IBM AI Engineering: IBM
  • LLM Engineering That Works: Prompting, Tuning, and Retrieval: Coursera
  • AI Engineering: Scrimba

Skills you can learn in Machine Learning

Python Programming (33)
Tensorflow (32)
Deep Learning (30)
Artificial Neural Network (24)
Big Data (18)
Statistical Classification (17)
Reinforcement Learning (13)
Algebra (10)
Bayesian (10)
Linear Algebra (10)
Linear Regression (9)
Numpy (9)

Frequently Asked Questions about Feature Engineering

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:

  1. Preview the first module of many feature engineering courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

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.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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