Predictive Modeling and Machine Learning with MATLAB
- 4.7
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Approx. 22 hours to complete
Course Summary
This course teaches the foundations of predictive modeling and machine learning, with a focus on practical applications in Python. Students will learn how to build and evaluate predictive models, as well as how to select and preprocess data for optimal performance.Key Learning Points
- Gain practical experience in building and evaluating predictive models
- Learn how to select and preprocess data for optimal performance
- Develop a solid understanding of machine learning concepts and techniques
Related Topics for further study
Learning Outcomes
- Build and evaluate predictive models using Python
- Select and preprocess data for optimal performance
- Understand and apply machine learning concepts and techniques
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge in Python
- Familiarity with linear algebra and statistics
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Machine Learning
- Machine Learning for Everyone
Related Education Paths
Related Books
Description
In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do.
Outline
- Creating Regression Models
- Practical Data Science with MATLAB
- Instructor Introduction
- Introduction to Supervised Machine Learning
- Introduction to the Taxi Data
- Creating and Cleaning Features
- Introduction to Regression
- Using the Regression Learner App
- Customizing Model Parameters
- Evaluating Regression Models
- Evaluate Your Model in MATLAB
- Summary of Regression
- Download and Install MATLAB
- Data and Code Files
- Supervised Machine Learning Reference
- Introduction to Module 1
- Variables in the Taxi Data
- Note regarding updates to MATLAB
- Summary of Regression Models
- Regression Metrics
- Feature Engineering Review
- Train a Regression Model
- Apply the Regression Workflow
- Creating Classification Models
- Introduction to Classification
- Using the Classification Learner App
- Evaluating Classification Models
- Evaluating Classification Models in MATLAB
- Training a Multiclass Model
- Summary of Classification
- Introduction to Module 2
- Note regarding updates to MATLAB
- Summary of Classification Models
- Binary Classification Metrics Reference
- Evaluate and Customize Classification Models
- Multiclass Classification Metrics Reference
- Customizing Multiclass Models
- Train a Classification Model
- Apply The Classification Workflow
- Applying the Supervised Machine Learning Workflow
- Addressing Underfitting and Overfitting
- Using Validation Data During Training
- Embedded Methods for Feature Selection
- Using Regularization to Prevent Overfitting
- Introduction to Ensemble Models
- Training Ensemble Models
- Introduction to Hyperparameters
- Optimizing Hyperparameters
- Summary of Module 3
- Introduction to Module 3
- Examining Bias Variance Trade-off
- Practice Partitioning Data
- Using Wrapper Methods to Select Features
- Introduction to the Course Project
- Practice Reducing Model Complexity
- Applying Ensemble Models
- Advanced Topics and Next Steps
- Handling Class Imbalance
- Reducing Specific Errors Using Cost Matrices
- Integrating Your Model
- A Discussion with Heather
- Summary of Predictive Modeling and Machine Learning
- Introduction to Module 4
- Sampling Data
- Practice Handling Class Imbalance
- Oversampling the Minority Class
- Examples of Integrating Machine Learning Models
- Automated Machine Learning
- Provide Feedback on Your Course Experience
- Practice Reducing Prediction Errors
- Quiz: Advanced Topics and Next Steps
Summary of User Reviews
Discover the art of Predictive Modeling and Machine Learning with Coursera. This course has received positive feedback from users for its comprehensive curriculum and real-world applications. Many users found the course to be a great starting point for enhancing their skills in the field of data science.Key Aspect Users Liked About This Course
The course has a comprehensive curriculum with real-world applications.Pros from User Reviews
- Great starting point for enhancing skills in data science.
- Hands-on exercises and assignments help to reinforce concepts.
- Instructors are knowledgeable and engaging.
- The course is well-structured and easy to follow.
- The course is self-paced, allowing users to take their time and review concepts as needed.
Cons from User Reviews
- Some users found the course to be too basic.
- The course can be challenging for those without a background in statistics or programming.
- The course is quite lengthy and may require a significant time commitment.
- Some users found the course to be too theoretical without enough practical applications.
- The cost of the course may be prohibitive for some users.
Heather Gorr
4.7 Raiting
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- 3.9
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