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