Course Summary
This course teaches you how to create meaningful predictive models by combining statistical and machine learning techniques. You will learn how to select the right data, choose the right model, and interpret the results in a meaningful way.Key Learning Points
- Learn how to combine statistical and machine learning techniques for predictive modeling
- Understand how to select the right data and choose the right model
- Interpret the results of your models in a meaningful way
Related Topics for further study
Learning Outcomes
- Create meaningful predictive models that can be applied to real-world problems
- Select the right data and choose the right model for your analysis
- Interpret the results of your models in a meaningful way
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics and machine learning
- Familiarity with programming languages such as R or Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Assignments
Similar Courses
- Practical Time Series Analysis
- Applied Data Science with Python
- Data Science Essentials
Related Education Paths
Related Books
Description
This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
Knowledge
- Understand the definitions of simple error measures (e.g. MSE, accuracy, precision/recall).
- Evaluate the performance of regressors / classifiers using the above measures.
- Understand the difference between training/testing performance, and generalizability.
- Understand techniques to avoid overfitting and achieve good generalization performance.
Outline
- Week 1: Diagnostics for Data
- Introduction to Course 3: Meaningful Predictive Modeling
- Motivation Behind the MSE
- Regression Diagnostics: MSE and R²
- Over- and Under-Fitting
- Classification Diagnostics: Accuracy and Error
- Classification Diagnostics: Precision and Recall
- Syllabus
- Setting Up Your System
- (Optional) Additional Resources and Recommended Readings
- Course Materials
- Review: Regression Diagnostics
- Review: Classification Diagnostics
- Diagnostics for Data
- Week 2: Codebases, Regularization, and Evaluating a Model
- Setting Up a Codebase for Evaluation and Validation
- Model Complexity and Regularization
- Adding a Regularizer to our Model, and Evaluating the Regularized Model
- Evaluating Classifiers for Ranking
- Review: Setting Up a Codebase
- Review: Regularization
- Review: Evaluating a Model
- Codebases, Regularization, and Evaluating a Model
- Week 3: Validation and Pipelines
- Validation
- “Theorems” About Training, Testing, and Validation
- Implementing a Regularization Pipeline in Python
- Guidelines on the Implementation of Predictive Pipelines
- Review: Validation
- Review: Predictive Pipelines
- Predictive Pipelines
- Final Project
- Project Description
- Where to Find Datasets
Summary of User Reviews
Discover the key to creating meaningful predictive models with this highly rated course on Coursera. Students praised the course for its comprehensive approach to data analysis and its practical, real-world examples.Key Aspect Users Liked About This Course
Real-world examplesPros from User Reviews
- Comprehensive approach to data analysis
- Practical real-world examples
- Engaging and knowledgeable instructor
- Well-structured course content
- Great for both beginners and advanced learners
Cons from User Reviews
- Course material can be overwhelming at times
- Not enough emphasis on big data technologies
- No hands-on assignments or projects
- Lack of interaction with other students
- Some lectures are too theoretical and difficult to follow
Julian McAuley
4.4 Raiting
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