Episode Synopsis "4.5 - Linear Regression With Multiple Variables | Features And Polynomial Regression"

 

Listen "4.5 - Linear Regression With Multiple Variables | Features And Polynomial Regression"

More episodes of the podcast AN ML on C

• 6.7 - Logistic Regression | MultiClass Classification OneVsAll
• Lecture 6.6 - Logistic Regression | Advanced Optimization
• 6.5 - Logistic Regression | Simplified Cost Function And Gradient Descent
• 6.4 - Logistic Regression | Cost Function
• 6.3 - Logistic Regression | Decision Boundary
• 6.2 - Logistic Regression | Hypothesis Representation
• 6.1 - Logistic Regression | Classification
• 4.7 - Linear Regression With Multiple Variables | Normal Equation Non Invertibility
• 4.6 - Linear Regression With Multiple Variables | Normal Equation
• 4.5 - Linear Regression With Multiple Variables | Features And Polynomial Regression
• 4.4 - Linear Regression With Multiple Variables | Gradient In PracticeaI | Learning Rate
• 4.3 - Linear Regression With Multiple Variables | Gradient In PracticaI Feature Scaling
• 4.2 - Linear Regression With Multiple Variables -- (Gradient Descent For Multiple Variables)
• 4.1 - Linear Regression With Multiple Variables - (Multiple Features)
• 3.6 - Linear Algebra Review | Inverse And Transpose
• Lecture 3.5 - Linear Algebra Review | Matrix Multiplication Properties
• Lecture 3.4 - Linear Algebra Review | Matrix-Matrix Multiplication
• Lecture 3.3 - Linear Algebra Review | Matrix Vector Multiplication
• Lecture 3.2 - Linear Algebra Review | Addition And Scalar Multiplication
• Lecture 3.1 - Linear Algebra Review | Matrices And Vectors
• Lecture 2.8 - What's Next
• Lecture 2.7 - Linear Regression With One Variable | Gradient Descent For Linear Regression
• Lecture 2.6 - Linear Regression With One Variable | Gradient Descent Intuition
• Lecture 2.5 - Linear Regression With One Variable | Gradient Descent
• Lecture 2.4 - Linear Regression With One Variable | Cost Function Intuition #2
• Lecture 2.3 - Linear Regression With One Variable | Cost Function Intuition #1
• Lecture 2.2 - Linear Regression With One Variable | CostFunction
• Lecture 2.1 - Linear Regression With One Variable | Model Representation
• Lecture 1.3 - Introduction Unsupervised Learning
• Lecture 1.2 - Introduction Supervised Learning
• Lecture 1.1 - Introduction What Is Machine Learning