## Course curriculum

• 1

### Chapter 1: Introductory Requirements

• Code 4 Tomorrow

• Hello!

• Libraries: Pandas, Numpy, ScikitLearn and Matplotlib

• Python Review: Loops, Sets, Tuples and Dictionaries

• Pandas and Data: Dataframes and Pandas Methods

• Quiz #1: Python Review

• 2

### Chapter 2: Machine Learning Overview

• The Machine Learning Pipeline

• Kinds of Learning

• What can Machine Learning Do?

• Quiz #2: An Overview of Machine Learning

• 3

### Chapter 3: Numpy/Scipy/MatPlotLib Functions

• Pandas 1: Importing Data

• Pandas 2: Dataframe Operations

• Pandas Quiz

• MatPlotLib 1: How to Scatter Plot

• MatPlotLib 2: How to Plot

• MatPlotLib Quiz

• Math Review: Vectors and Matrices

• Quiz: Matrices and Vector Operations

• NumPy 1: 1D and 2D NumPy Arrays

• NumPy 2: Linear Algebra

• NumPy Quiz

• 4

### Chapter 4: Training and Testing Data

• Training and Testing

• Overfitting and Underfitting

• Train/Test Split

• Train/Test Strategies

• 5

### Chapter 5: Simple Linear Regression

• What is Regression?

• Simple Linear Regression

• Optimization Approaches: Gradient Descent and Ordinary Least Squares

• Simple Linear Regression: Time, Marbles and Motion

• Quiz: Optimization Approaches

• 6

### Chapter 6: Evaluation Metrics

• Intro to Evaluation Metrics

• Eval. Metric 1: Mean Absolute Error

• Eval. Metric 2: Mean Squared Error

• Eval. Metric 3: Root Mean Squared Error

• Eval. Metric 4: Relative Absolute Error

• Eval. Metric 5: Relative Squared Error

• Eval. Metric 6: R squared Score

• Quiz: Evaluation Metrics

• 7

### Chapter 7: Multiple Linear Regression

• General Overview

• The Prediction Equation

• Key Tips Before Execution

• Multiple Linear Regression: Housing Prices in King County

• 8

### Chapter 8: Non-Linear Regression

• Polynomial Regression: The Idea

• Polynomial vs Multiple Regression

• PolynomialFeatures and Fit_transform

• Polynomial Regression

• Some Other Non-Linear Models

• Exponential and Sigmoidal Regression