Course curriculum

  • 1

    Welcome to Machine Learning for Humans! - Course Orientation

    • Course Welcome
    • WELCOME: Janet Uy, ASEAN Lead Data Scientist at Oracle Singapore
    • Course Overview
    • How To Use This Learning Platform
    • Meet the Featured Experts
    • Requirements for Certificate of Completion
    • Glossary of Terms
  • 2

    CHAPTER 1 - The Rise of a Data-Driven World

    • Chapter 1 Instructions
    • LEARNING SECTION 1: The Story of Data
    • LEARNING SECTION 2: Machine Learning 101
    • KNOWLEDGE CHECK: Chapter 1
  • 3

    CHAPTER 2 - How to Conduct a Machine Learning Project [Part 1]

    • Chapter 2 Instructions
    • Introduction to Framing the Problem by Kenneth Soo
    • LEARNING SECTION 1: Framing the Problem
    • Introduction to Data Collection & Preparation by Kenneth Soo
    • LEARNING SECTION 2: Data Collection & Preparation
    • Introduction to Feature Selection & Engineering by Kenneth Soo
    • LEARNING SECTION 3: Feature Selection & Engineering
    • KNOWLEDGE CHECK: Chapter 2
  • 4

    Chapter 3 - How to Conduct a Machine Learning Project [Part 2]

    • Chapter 3 Instructions
    • Introduction to Model Selection by Kenneth Soo
    • LEARNING SECTION 1: Model Selection
    • Introduction to Model Evaluation by Kenneth Soo
    • LEARNING SECTION 2: Model Evaluation
    • KNOWLEDGE CHECK: Chapter 3
    • CASE STUDY 1: Uber Technologies
    • CASE STUDY 1 QUIZ: Uber Technologies
  • 5

    CHAPTER 4 - How Machines Predict Values

    • Chapter 4 Instructions
    • Introduction to Linear Regression by Ritchie Ng
    • LEARNING SECTION 1: Linear Regression
    • LEARNING SECTION 2: Multiple Linear Regression
    • KNOWLEDGE CHECK : Chapter 4
  • 6

    CHAPTER 5 - How Machines Predict Categories

    • Chapter 5 Instructions
    • Introduction to Logistic Regression by Ritchie Ng
    • LEARNING SECTION 1: Logistic Regression
    • Introduction to K-nearest Neighbours by Elaine Liew
    • LEARNING SECTION 2: K-nearest Neighbours
    • Introduction to Support Vector Machine by Elaine Liew
    • LEARNING SECTION 3: Support Vector Machine
    • Introduction to Decision Trees by Elaine Liew
    • LEARNING SECTION 4: Decision Trees
    • Introduction to Naive Bayes Classifier by Elaine Liew
    • LEARNING SECTION 5: Naive Bayes Classifier
    • KNOWLEDGE CHECK: Chapter 5
  • 7

    CHAPTER 6 - How Machines Predict Similarities

    • Chapter 6 Instructions
    • Introduction to K-means Clustering by Janet Uy
    • LEARNING SECTION 1: K-means Clustering
    • Introduction to Association Rules by Janet Uy
    • LEARNING SECTION 2: Association Rules
    • KNOWLEDGE CHECK: Chapter 6
  • 8

    CHAPTER 7 - Advanced Modelling Techniques

    • Chapter 7 Instructions
    • Introduction to Artificial Neural Networks by Ritchie Ng
    • LEARNING SECTION 1: Artificial Neural Networks
    • Introduction to Reinforcement Learning by Ritchie Ng
    • LEARNING SECTION 2: Reinforcement Learning
    • KNOWLEDGE CHECK: Chapter 7
  • 9

    CHAPTER 8 - Key Considerations when Machines Learn

    • Chapter 8 Instructions
    • Introduction to Risk & Ethics by Janet Uy
    • LEARNING SECTION 1: Risk and Concerns
    • LEARNING SECTION 2: Ethical Considerations
    • KNOWLEDGE CHECK: Chapter 8
    • CASE STUDY 2: Strategeion
    • CASE STUDY 2 QUIZ: Strategeion
  • 10

    Final Steps

    • Course Wrap-up