1. Linear Regression

Syllabus Points Covered

Software automation

Algorithms in machine learning

• Distinguish between artificial intelligence (AI) and ML

• Investigate common applications of key ML algorithms

  • data analysis and forecasting

• Describe types of algorithms associated with ML

  • linear regression

Programming for automation

• Design, develop and apply ML regression models using an OOP to predict numeric values

  • linear regression

Chapter Contents

• 1.1. Artificial Intelligence and Machine Learning
  • 1.1.1. Artificial Intelligence
  • 1.1.2. Machine Learning
  • 1.1.3. Glossary
• 1.2. Supervised vs Unsupervised Learning
  • 1.2.1. Supervised Learning
  • 1.2.2. Unsupervised Learning
  • 1.2.3. Glossary
• 1.3. Linear Regression
  • 1.3.1. Glossary
• 1.4. Measuring Error
• 1.5. Reading in Data With Pandas
• 1.6. Scatter Plots
• 1.7. Visualising Data
• 1.8. Fitting a Linear Regression Model
• 1.9. Line Plots
• 1.10. Plotting Functions and Visualising Models
• 1.11. Making Predictions
• 1.12. Measuring Error Using the MSE
  • 1.12.1. MSE in Python
  • 1.12.2. Interpreting the MSE
  • 1.12.3. Glossary
• 1.13. Extension: Fitting The Model
• 1.14. Multiple Linear Regression
  • 1.14.1. Glossary