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Coursera - Supervised Machine Learning Regression and Classification 2025-1
Course modified date: 29 Jan 2025
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Coursera - Supervised Machine Learning Regression and Classification 2025-1
Course content
Sections:
3
•
Activities:
0
•
Resources:
44
- 01_welcome to machine learning
- 02_applications of machine learning
- 01_what is machine learning
- 02_supervised learning part 1
- 03_supervised learning part 2
- 04_unsupervised learning part 1
- 05_unsupervised learning part 2
- 06_jupyter notebooks
- 01_linear regression model part 1
- 02_linear regression model part 2
- 03_cost function formula
- 04_cost function intuition
- 05_visualizing the cost function
- 06_visualization examples
- 01_gradient descent
- 02_implementing gradient descent
- 03_gradient descent intuition
- 04_learning rate
- 05_gradient descent for linear regression
- 06_running gradient descent
- 01_multiple features
- 02_vectorization part 1
- 03_vectorization part 2
- 04_gradient descent for multiple linear regression
- 01_feature scaling part 1
- 02_feature scaling part 2
- 03_checking gradient descent for convergence
- 04_choosing the learning rate
- 05_feature engineering
- 06_polynomial regression
- 01_motivations
- 02_logistic regression
- 03_decision boundary
- 01_cost function for logistic regression
- 02_simplified cost function for logistic regression
- 01_gradient descent implementation
- 01_the problem of overfitting
- 02_addressing overfitting
- 03_cost function with regularization
- 04_regularized linear regression
- 05_regularized logistic regression
- 01_important-reminder-about-end-of-access-to-lab-notebooks_instructions
- 01_andrew ng and fei fei li on human centered ai
- 01_acknowledgments_instructions