Data Processing and Feature Engineering with MATLAB
- 4.7
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Approx. 18 hours to complete
Course Summary
Learn how to create powerful features for machine learning models using MATLAB. This course covers a range of techniques for feature engineering, including statistical measures, image processing, and natural language processing.Key Learning Points
- Understand the importance of feature engineering in machine learning
- Learn how to extract and manipulate data to create effective features
- Explore a variety of techniques for feature engineering, including statistical measures, image processing, and natural language processing
Related Topics for further study
Learning Outcomes
- Understand the importance of feature engineering in machine learning
- Learn a variety of techniques for feature engineering
- Be able to apply feature engineering techniques to real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of MATLAB programming
- Familiarity with machine learning concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Science Essentials
- Machine Learning Fundamentals
- Deep Learning
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yann LeCun
Related Books
Description
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling. This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling.
Outline
- Surveying Your Data
- Practical Data Science with MATLAB
- Overview of Data Processing and Feature Engineering
- Instructor Introduction
- Introduction to Module 1
- Introduction to the Flights Dataset
- Exploring the Flights Dataset
- Describing Distributions
- Examples of Distributions
- Visualizing Multidimensional Data
- Summary of Module 1: Surveying Your Data
- Download and Install MATLAB
- Data and Code Files
- Variables in the Flights Dataset
- Practice Visualizing Multidimensional Data
- Understanding the Flights Dataset
- Module 1 Quiz
- Organizing Your Data
- Introduction to Module 2: Organizing Your Data
- Working with Strings
- Working with Dates and Times
- Importing Multiple Data Files
- Combining Data
- Joining Tables
- Sorting Your Data
- Summary of Module 2: Organizing Your Data
- Practice Working with Strings
- Practice Using Dates and Times
- Practice Working with Strings
- Quiz 2: Organizing Your Data
- Cleaning Your Data
- Introduction to Module 3: Cleaning Your Data
- Identifying Missing Data
- Handling Missing Data
- Identifying Outliers
- Investigating Outliers
- Normalizing Data
- Examples of Normalizing Data
- Smoothing Data
- Summary of Module 3: Cleaning Your Data
- Practice Working with Outliers
- Cleaning Data: Analyzing Flight Volume
- Practice Quiz: Putting it all Together
- Quiz 3: Cleaning Your Data
- Finding Features that Matter
- Introduction to Module 4: Finding Features that Matter
- Introduction to Feature Engineering
- Introduction to Unsupervised Learning
- Introduction to Clustering Algorithms
- Evaluating Features
- Introduction to Dimensionality Reduction and PCA
- Summary of Module 4: Finding Features that Matter
- Examples of Feature Engineering
- Practice Clustering Data
- K-means Clustering
- Applying Filter Methods
- Applying PCA
- Quiz 4: Finding Features that Matter
- Domain-Specific Feature Engineering
- Introduction to Module 5: Domain-Specific Feature Engineering
- Feature Engineering Workflow
- Synchronizing Data with Timetables
- Summary Statistics as Features
- Finding Peaks
- Feature Engineering and Clustering with Images
- Feature Engineering with Text
- Feature Engineering with Storm Events
- Summary of Module 5: Domain-Specific Feature Engineering
- Summary of Data Processing and Feature Engineering
- Practice using Summary Stats as Features
- Practice Working with Images
- Modeling Using Qualitative Descriptions
- Provide Feedback on Your Course Experience
- Practice Finding Peaks
- Quiz 5: Domain-Specific Feature Engineering
Summary of User Reviews
Learn about feature engineering techniques using MATLAB with this comprehensive course. Students have praised the course for its thoroughness and practical approach to the subject.Key Aspect Users Liked About This Course
The course has been praised by many users for its practical approach to feature engineering.Pros from User Reviews
- Thorough coverage of feature engineering techniques
- Practical examples and hands-on exercises
- Well-structured course material
- Engaging and knowledgeable instructors
- Useful for beginners and advanced learners alike
Cons from User Reviews
- Some users found the pace of the course to be too slow
- Not suitable for those without prior MATLAB experience
- Some users would have liked more advanced topics to be covered
- Limited interaction with instructors and other students
- Some technical issues with the course platform reported by users
Adam Filion
4.7 Raiting
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