Course curriculum

  • 1

    Introduction

    • Course Introduction

    • What is this Course Exactly?

    • Course Outcomes

    • Course Structure

    • What is an Algorithm?

  • 2

    Data Preparation

    • Loading a CSV File

  • 3

    Scale Data

    • Scale Your Data: Normalization

    • Scale Your Data: Standardization

  • 4

    Baseline Models

    • Establishing a Baseline

    • Random Prediction Algorithm

    • Zero Rule Algorithm
  • 5

    Algorithm Evaluation Methods

    • Algorithm Evaluation Methods

    • Train-Test Split

    • K-Fold Cross-Validation

    • How to Choose a Resampling Algorithm?

  • 6

    Evaluation Metrics

    • Evaluation Metrics

    • Classification Accuracy

    • Confusion Matrix

    • Regression Metrics

    • K-Fold Cross-Validation Defined
  • 7

    Linear Algorithms

    • Algorithm Test Harness - Train-Test-Split

    • Algorithm Test Harness - K-Fold

    • Simple Linear Regression

    • Simple Linear Regression Case Study

    • Simple Linear Regression Case Study: Part 2

    • Multivariate Linear Regression Case Study

    • Demo: Multivariate Linear Regression Case Study

    • Demo: Linear Regression on Wine Quality Dataset

    • Logistic Regression Defined

    • Demo: Logistic Regression: Make Predictions

    • Demo: Logistic Regression : Estimating Coefficients

    • Demo: Logistic Regression : Diabetes Dataset

    • The Perceptron

    • Demo: The Perceptron: Make Predictions

    • Demo: The Perceptron: Training Weights

    • Demo: The Perceptron: Sonar Dataset

  • 8

    Nonlinear Algorithms

    • Classification and Regression Trees

    • Demo: CART : Creating the Gini Index

    • Demo: CART : Creating the Splits

    • Demo: CART : Evaluating the Splits

    • CART : Building the Tree

    • Demo: CART : Recursive Splitting

    • Demo: CART : Assembling the Tree

    • Demo: CART : CART to Banknote Dataset

    • Naïve Bayes

    • Demo: Naïve Bayes: Separate by Class

    • Demo: Naïve Bayes: Summarize the Dataset

    • Demo: Naïve Bayes: Summarize Data by Class

    • Demo: Naïve Bayes: Gaussian Probability Density Function

    • Demo: Naïve Bayes: Class Probabilities

    • Demo: Naïve Bayes: Iris Flowers Dataset

    • K-Nearest Neighbors

    • Demo: KNN: Calculate Euclidean Distance

    • Demo: KNN: Get Nearest Neighbors

    • Demo: KNN: Making Predictions

    • Demo: KNN: Iris Dataset

    • Artificial Neural Network

    • Demo: Artificial Neural Network: Initialize Network

    • Demo: Artificial Neural Network: Forward Propagation

    • Demo: Artificial Neural Network: Error Backpropagation

    • Demo: Artificial Neural Network: Training the Network

    • Demo: Artificial Neural Network: Predictions

    • Demo: Artificial Neural Network: Final Model on Real Dataset