Software Automation

• 1. Linear Regression
  • 1.1. Artificial Intelligence and Machine Learning
  • 1.2. Supervised vs Unsupervised Learning
  • 1.3. Linear Regression
  • 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
  • 1.13. Extension: Fitting The Model
  • 1.14. Multiple Linear Regression
• 2. Polynomial and Logistic Regression
  • 2.1. Polynomial and Logistic Regression
  • 2.2. Polynomial Regression
  • 2.3. The Relationship Between Linear Regression and Polynomial Regression
  • 2.4. Building a Polynomial Regression Model
  • 2.5. Extension: Selecting The Polynomial Degree
  • 2.6. Logistic Regression
  • 2.7. Measuring Error
  • 2.8. Building a Logistic Regression Model
  • 2.9. Predicting With A Logistic Regression Model
  • 2.10. Extension: Further Classification Metrics
  • 2.11. Extension: Multiple Logistic Regression
• 3. Decision Trees
  • 3.1. Decision Trees
  • 3.2. Building a Classification Tree
  • 3.3. Classifying With a Classification Tree
  • 3.4. Node Impurity and Tree Height
  • 3.5. A Semi-Supervised Model
  • 3.6. Random Forests
  • 3.7. Extension: Building a Classification Tree
  • 3.8. Extension: Interpreting The Output Graph
  • 3.9. Extension: Predicting With a Classification Tree
  • 3.10. Building a Regression Tree
  • 3.11. Predicting With a Regression Tree
  • 3.12. Extension: Building and Predicting With A Regression Tree
  • 3.13. Semi-Supervised Learning and Random Forests
  • 3.14. Interpreting Decision Trees
• 4. K-Nearest Neighbours and K-Means Clustering
  • 4.1. K-Nearest Neighbours and K-Means Clustering
  • 4.2. Distance and Similarity
  • 4.3. Extension: The Problem With Distance Similarity
  • 4.4. KNN Regression 1D
  • 4.5. Visualising KNN Regression 1D (k = 1)
  • 4.6. Extension: Visualising KNN Regression 1D (k = 2)
  • 4.7. KNN Regression 2D
  • 4.8. Extension: Building a KNN Regression Model
  • 4.9. Extension: Selecting The Value of k
  • 4.10. KNN Classification
  • 4.11. Extension: Image Data
  • 4.12. Extension: Building a KNN Classification Model
  • 4.13. KNN 2D
  • 4.14. Unsupervised Learning: Clustering
  • 4.15. Extension: The K-means Clustering Algorithm
  • 4.16. Extension: Building a K-means Clustering Model
  • 4.17. Extension: Text Data
• 5. Neural Networks
  • 5.1. Deep Learning
  • 5.2. Neural Networks
  • 5.3. RGB to Hue and Saturation
  • 5.4. Information Flow: Making a Prediction
  • 5.5. Calculating Errors
  • 5.6. Training a Neural Network
  • 5.7. Building a Neural Network for Regression
  • 5.8. Problem and Model Analysis
  • 5.9. Neural Networks for Classification
  • 5.10. Building a Neural Network For Classification
  • 5.11. More Advanced Neural Networks
• 6. Reinforcement Learning
  • 6.1. Reinforcement Learning
• 7. Design, Applications and Impact
  • 7.1. Types Of Machine Learning Summary
  • 7.2. Exercise: Applications of Machine Learning Algorithms
  • 7.3. ML in DevOPS, RPA and BPA
  • 7.4. MLOps
  • 7.5. Bias in AI
  • 7.6. How Cultural Protocols and Belief Systems Impact AI
  • 7.7. How Patterns in Human Behaviour Influence ML and AI Software Development
  • 7.8. The Impacts of Automation on the Individual, Society and the Environment