The following are Machine Learning algorithms I learned during the coursera Machine Learning course I took from Stanford during the summer of 2021.
- Supervised Learning
- Linear Regression
- Gradient Descent
- Supervised Learning
- Multivariable Regression
- Multivariable Gradient Descent
- Mean Normalization
- Supervised Learning
- Classification
- Logistic Regression
- Supervised Learning
- Classification
- Logistic Regression
- Regularization
- Supervised Learning
- Classification
- Naive Bayes (One Vs All)
- Regularization
- Neural Networks
- Feedforward
- Backpropagation
- Supervised Learning
- Linear Regression
- Bias/Variance
- Learning Curves
- Selecting Lambda
- Training/CV/Test Data
- Supervised Learning
- Classification
- Support Vector Machines
- Linear Kernel
- Supervised Learning
- Classification
- Support Vector Machines
- Gaussian Kernel (Non-Linear)
- Supervised Learning
- Classification
- Support Vector Machines
- Gaussian Kernel (Non-Linear)
- Selecting the Best Values for C (1/Lambda) and the Varriance
- Supervised Learning
- Classification
- Support Vector Machines
- Linear Kernel
- Regular Expressions
- Supervised Learning
- Clustering
- K-Means
- Supervised Learning
- Clustering
- K-Means