Week 1: Introduction to Machine Learning-Overview of Machine Learning
Welcome to machine learning!
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Applications of machine learning
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Week 1: Introduction to Machine Learning-Supervised vs. Unsupervised Machine Learning
What is machine learning?
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Supervised learning part 1
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Supervised learning part 2
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Unsupervised learning part 1
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Unsupervised learning part 2
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Jupyter Notebooks
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Week 1: Introduction to Machine Learning-Regression Model
Linear regression model part 1
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Linear regression model part 2
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Cost function formula
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Cost function intuition
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Visualizing the cost function
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Visualization examples
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Week 1: Introduction to Machine Learning-Train the model with gradient descent
Gradient descent
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Implementing gradient descent
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Gradient descent intuition
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Learning rate
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Gradient descent for linear regression
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Running gradient descent
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Week 2: Regression with multiple input variables-Multiple linear regression
Multiple features
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Vectorization part 1
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Vectorization part 2
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Gradient descent for multiple linear regression
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Week 2: Regression with multiple input variables-Gradient descent in practice
Feature scaling part 1
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Feature scaling part 2
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Checking gradient descent for convergence
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Choosing the learning rate
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Feature engineering
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Polynomial regression
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Week 3: Classification-Classification with logistic regression
Motivations
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Logistic regression
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Decision boundary
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Week 3: Classification-Cost function for logistic regression
Cost function for logistic regression
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Simplified Cost Function for Logistic Regression
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Week 3: Classification-Gradient descent for logistic regression
Gradient Descent Implementation
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Week 3: Classification-The problem of overfitting
The problem of overfitting
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Addressing overfitting
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Cost function with regularization
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Regularized linear regression
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Regularized logistic regression
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Week 3: Classification-End of Access to Lab Notebooks
[IMPORTANT] Reminder about end of access to Lab Notebooks
Week 3: Classification-Conversations with Andrew (Optional)
Andrew Ng and Fei-Fei Li on Human-Centered AI
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Week 3: Classification-Acknowledgments
Acknowledgments