Course curriculum

  • 1

    Chapter 1: Introductory Requirements

    • Code 4 Tomorrow

    • Hello!

    • Setting Up Google Colab

    • 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