Course Summary
This course teaches the fundamental concepts and techniques for feature engineering, a crucial step in the process of building effective machine learning models. Through hands-on exercises and real-world case studies, you will learn how to extract, transform, and select features from diverse data sources to improve model accuracy and interpretability.Key Learning Points
- Understand the importance of feature engineering in machine learning
- Learn techniques for data cleaning, transformation, and selection
- Apply feature engineering concepts in real-world case studies
Related Topics for further study
Learning Outcomes
- Apply feature engineering techniques to improve model accuracy
- Identify and extract relevant features from diverse data sources
- Clean and transform data to prepare it for machine learning models
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of machine learning concepts
- Familiarity with programming in Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Hands-on exercises
- Real-world case studies
Similar Courses
- Applied Data Science with Python
- Applied Machine Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Fei-Fei Li
Related Books
Description
Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering where we will discuss good vs bad features and how you can preprocess and transform them for optimal use in your models.
Knowledge
- Compare the key required aspects of a good feature
- Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep
- Combine and create new feature combinations through feature crosses
- Understand and apply how TensorFlow transforms features
Outline
- Introduction to Course
- Introduction to Course
- Getting Started with Google Cloud and Qwiklabs
- Raw Data to Features
- An Overview of Feature Engineering
- Raw Data to Features
- Good vs Bad Features
- Features Known at Prediction-time
- Features should be Numeric
- Features Should Have Enough Examples
- Bringing Human Insight
- Representing Features
- ML vs Statistics
- Lab Intro: Basic Feature Engineering in BQML
- Lab Intro: Basic Feature Engineering in Keras
- Resources
- Raw Data to Features and Good vs Bad Features
- Prediction time, Numeric, Enough Examples, Human sight
- Representing Features questions
- Feature Engineering
- Preprocessing and feature creation
- Beam and Dataflow
- Lab Intro: Simple Dataflow Pipeline
- Lab Solution: Simple Dataflow Pipeline
- Data Pipelines that Scale
- Lab Intro: MapReduce in Dataflow
- Lab Solution: MapReduce in Dataflow
- Preprocessing with Cloud Dataprep
- Lab Intro: Computing Time-Windowed Features in Cloud Data
- Lab Solution: Computing Time-Windowed Features in Cloud Dataprep
- Resources
- Apache Beam and Cloud Dataflow
- Preprocessing with Cloud Dataprep
- Feature Crosses
- Introducing Feature Crosses
- What is a Feature Cross
- Discretization
- Memorization vs. Generalization
- Taxi colors
- Lab Intro: Feature Crosses to create a good classifier
- Lab Solution: Feature Crosses to create a good classifier
- Sparsity + Quiz
- Lab Intro: Too Much of a Good Thing
- Lab Solution: Too Much of a Good Thing
- Implementing Feature Crosses
- Embedding Feature Crosses
- Feature Creation in TensorFlow
- Feature Creation in DataFlow
- Lab Intro: Improve ML Model with Feature Engineering
- Lab Solution: ML Fairness Debrief
- Lab Intro: Advanced Feature Engineering in BQML
- Lab Intro: Advanced Feature Engineering in Keras
- Resources
- Feature crosses
- Module Quiz
- TensorFlow Transform
- Introducing TensorFlow Transform
- TensorFlow Transform
- Analyze phase
- Transform phase
- Supporting serving
- Lab Intro: Exploring tf.transform
- Resources
- tf.transform
- Summary
- Summary
- Resources - Readings Compiled as PDF
- All Quiz Questions as a PDF
- Course Slides
- Course Quiz
Summary of User Reviews
Learn about feature engineering and its importance in machine learning with Coursera's comprehensive course. Users have praised the practicality and usefulness of the content, giving it a high rating. One key aspect that many users thought was good is the clear explanations and real-world examples provided throughout the course.Pros from User Reviews
- Clear explanations and real-world examples
- Comprehensive coverage of feature engineering
- Practical and useful content
- Engaging and interactive learning experience
- Flexible scheduling and pacing
Cons from User Reviews
- Some exercises are too simplistic
- Lack of depth in certain areas
- Limited interaction with instructors
- Some technical issues with the platform
- Course may be too basic for advanced learners
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