3. Software Automation

Legend

• Here - found in the module

• Moved - found in a different module

• Implicit - covered by the process of completing this or a different module

• Coming Soon - to be provided at a later date

• Not Planned - not covered in this book

3.1. Algorithms In Machine Learning

Investigate how machine learning (ML) supports automation through the use of DevOps, robotic process automation (RPA) and business process automation (BPA)

• ML in DevOPS, RPA and BPA

• MLOps

Distinguish between artificial intelligence (AI) and ML

• Artificial Intelligence and Machine Learning

Explore models of training ML

• Types Of Machine Learning Summary

Including:

supervised learning

• Supervised vs Unsupervised Learning

unsupervised learning

• Supervised vs Unsupervised Learning

• Unsupervised Learning: Clustering

• Extension: The K-means Clustering Algorithm

• Extension: Building a K-means Clustering Model

semi-supervised learning

• A Semi-Supervised Model

• Random Forests

• Semi-Supervised Learning and Random Forests

reinforcement learning

• Reinforcement Learning

Investigate common applications of key ML algorithms

Including:

data analysis and forecasting

• Exercise: Applications of Machine Learning Algorithms

Select examples:

• Predicting student marks using a multiple linear regression model: Multiple Linear Regression

• Predicting whether it will rain or not using a logistic regression model: Predicting With A Logistic Regression Model (code challenge)

• Predicting prices using a regression tree: Extension: Building and Predicting With A Regression Tree

virtual personal assistants

• Exercise: Applications of Machine Learning Algorithms

Example:

• Google AI: How Patterns in Human Behaviour Influence ML and AI Software Development (19:30 into the video Artificial intelligence and its ethics | DW Documentary 2020)

image recognition

• Exercise: Applications of Machine Learning Algorithms

Examples:

• Classifying digits using KNN classification: Extension: Image Data, Extension: Building a KNN Classification Model (code challenge)

• Classifying digits using a neural network: Building a Neural Network For Classification (code challenge), see also More Advanced Neural Networks > Convolution Neural Networks

Research models used by software engineers to design and analyse ML

Including

decision trees

• Decision Trees

• Building a Classification Tree

• Classifying With a Classification Tree

• Node Impurity and Tree Height

• A Semi-Supervised Model

• Random Forests

• Extension: Building a Classification Tree

• Extension: Interpreting The Output Graph

• Extension: Predicting With a Classification Tree

• Building a Regression Tree

• Predicting With a Regression Tree

• Extension: Building and Predicting With A Regression Tree

• Semi-Supervised Learning and Random Forests

• Interpreting Decision Trees

neural networks

• Deep Learning

• Neural Networks

• RGB to Hue and Saturation

• Information Flow: Making a Prediction

• Calculating Errors

• Training a Neural Network

• Problem and Model Analysis

• Neural Networks for Classification

• More Advanced Neural Networks

Describe types of algorithms associated with ML

Including:

linear regression

• Linear Regression

• Measuring Error

logistic regression

• Logistic Regression

• Measuring Error

K-nearest neighbour

• KNN Regression 1D

• Visualising KNN Regression 1D (k = 1)

• Extension: Visualising KNN Regression 1D (k = 2)

• KNN Regression 2D

• Extension: Building a KNN Regression Model

• Extension: Selecting The Value of k

• KNN Classification

• Extension: Image Data

• Extension: Building a KNN Classification Model

3.2. Programming For Automation

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

Including:

linear regression

• Fitting a Linear Regression Model

• Plotting Functions and Visualising Models

• Making Predictions

• Measuring Error Using the MSE

• Extension: Fitting The Model

• Multiple Linear Regression

polynomial regression

• Polynomial Regression

• The Relationship Between Linear Regression and Polynomial Regression

• Building a Polynomial Regression Model

• Extension: Selecting The Polynomial Degree

logistic regression

• Building a Logistic Regression Model

• Predicting With A Logistic Regression Model

• Extension: Further Classification Metrics

• Extension: Multiple Logistic Regression

Apply neural network models using an OOP to make predictions

• Building a Neural Network for Regression

• Building a Neural Network For Classification

3.3. Significance And Impact Of ML And AI

Assess the impact of automation on the individual, society and the environment

Including:

safety of workers

• The Impacts of Automation on the Individual, Society and the Environment

people with disability

• The Impacts of Automation on the Individual, Society and the Environment

the nature and skills required for employment

• The Impacts of Automation on the Individual, Society and the Environment

production efficiency, waste and the environment

• The Impacts of Automation on the Individual, Society and the Environment

the economy and distribution of wealth

• The Impacts of Automation on the Individual, Society and the Environment

Explore by implementation how patterns in human behaviour influence ML and AI software development

Including:

psychological responses

• How Patterns in Human Behaviour Influence ML and AI Software Development

patterns related to acute stress response

• How Patterns in Human Behaviour Influence ML and AI Software Development

cultural protocols

• How Cultural Protocols and Belief Systems Impact AI

belief systems

• How Cultural Protocols and Belief Systems Impact AI

Investigate the effect of human and dataset source bias in the development of ML and AI solutions

• Bias in AI