Coursera Machine Learning (ML) Practice Course Overview
Welcome to the "Practice" section of the Coursera Machine Learning course by Andrew Ng. Here, you can find hands-on exercises to reinforce your understanding of the concepts covered in the course.
Course Content
This section covers the following topics:
- Data Preprocessing
- Model Selection
- Model Training and Evaluation
- Regularization and Bias/Variance Tradeoff
- Hyperparameter Tuning
- Feature Engineering
- Model Interpretation
Key Takeaways
- Data Preprocessing: Learn how to handle missing values, scale features, and encode categorical variables.
- Model Selection: Explore various algorithms and choose the right one for your problem.
- Model Training and Evaluation: Understand the importance of cross-validation and how to measure model performance.
- Regularization and Bias/Variance Tradeoff: Learn about techniques to prevent overfitting and underfitting.
- Hyperparameter Tuning: Discover how to optimize model performance by adjusting hyperparameters.
- Feature Engineering: Find out how to create new features that improve model performance.
- Model Interpretation: Understand how to interpret your models and gain insights into their predictions.
Practice Exercises
To practice what you've learned, follow these exercises:
- Data Preprocessing Exercises
- Model Selection Exercises
- Model Training and Evaluation Exercises
- Regularization and Bias/Variance Tradeoff Exercises
- Hyperparameter Tuning Exercises
- Feature Engineering Exercises
- Model Interpretation Exercises
Resources
Visualization of Machine Learning Model
Here is an example of a simple linear regression model. This model predicts the price of a house based on its size.
Good luck with your practice exercises! 🎓