# jpandersen61/machine-learning architecture

> Architecture diagram of the GitHub repository jpandersen61/machine-learning, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-29). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/jpandersen61/machine-learning
- Repository: https://github.com/jpandersen61/machine-learning

## Overview

This repository is a collection of teaching notebooks covering Python, data analysis, machine learning, and generative AI—not a single application. The likely learner workflow is to open a topic notebook, work through its exercise or solution, and, for data-based lessons, use a corresponding CSV. With no README or sampled notebook source, dataset links and runtime behavior cannot be verified; the graph reflects only clear filename-level associations.

Main entry points: [`50StartUps_Load_Data.ipynb`](https://github.com/jpandersen61/machine-learning/blob/main/50StartUps_Load_Data.ipynb) (Startup Data Loading), [`Linear_Regression_Lifeexpectancy.ipynb`](https://github.com/jpandersen61/machine-learning/blob/main/Linear_Regression_Lifeexpectancy.ipynb) (Life Expectancy Regression), [`Classification_Diagnosing_Diabetes.ipynb`](https://github.com/jpandersen61/machine-learning/blob/main/Classification_Diagnosing_Diabetes.ipynb) (Diabetes Classification), [`Mandatory_assignment_Wine_Quality.ipynb`](https://github.com/jpandersen61/machine-learning/blob/main/Mandatory_assignment_Wine_Quality.ipynb) (Wine Quality)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_python["Python Foundations"]
  node_python1["Python Basics 1"]
  node_python2["Python Basics 2"]
  node_initial["Python Exercise"]
end

subgraph group_supervised["Supervised Learning"]
  node_regression["Linear Regression"]
  node_startups["Startup Regression<br/>[50StartUps.ipynb]"]
  node_startupdata["Startup Data Loading"]
  node_startupcsv[("Startup Data<br/>[50_Startups.csv]")]
  node_housing["Housing Exercises"]
  node_life["Life Expectancy Regression"]
  node_lifecsv[("Life Expectancy Data")]
  node_diabetes["Diabetes Classification"]
  node_diabetescsv[("Diabetes Data<br/>[diabetes.csv]")]
  node_mnist["MNIST Classification"]
  node_fashion["Fashion Classification"]
  node_wine["Wine Quality"]
  node_winecsv[("Wine Quality Data<br/>[WineQT.csv]")]
  node_svm["Iris SVM"]
end

subgraph group_unsupervised["Unsupervised Learning"]
  node_cluster["Clustering Lessons"]
  node_mallcsv[("Customer Data<br/>[Mall_Customers.csv]")]
end

subgraph group_genai["Generative AI"]
  node_chat["Chat Sequence"]
  node_structured["Structured Output"]
  node_news["News Generator"]
end

node_learner(("Learner"))

node_learner -->|"opens"| node_python1
node_learner -->|"opens"| node_python2
node_learner -->|"opens"| node_initial
node_learner -->|"opens"| node_regression
node_learner -->|"opens"| node_startups
node_learner -->|"opens"| node_startupdata
node_learner -->|"opens"| node_housing
node_learner -->|"opens"| node_life
node_learner -->|"opens"| node_diabetes
node_learner -->|"opens"| node_mnist
node_learner -->|"opens"| node_fashion
node_learner -->|"opens"| node_wine
node_learner -->|"opens"| node_svm
node_learner -->|"opens"| node_cluster
node_learner -->|"opens"| node_chat
node_learner -->|"opens"| node_structured
node_learner -->|"opens"| node_news
node_startupdata -.->|"likely loads"| node_startupcsv
node_life -.->|"likely uses"| node_lifecsv
node_diabetes -.->|"likely uses"| node_diabetescsv
node_wine -.->|"likely uses"| node_winecsv
node_cluster -.->|"likely uses"| node_mallcsv

click node_python1 "https://github.com/jpandersen61/machine-learning/blob/main/Python_Basic_1.ipynb"
click node_python2 "https://github.com/jpandersen61/machine-learning/blob/main/Python_Basic_2.ipynb"
click node_initial "https://github.com/jpandersen61/machine-learning/blob/main/Initial_Python_Execise.ipynb"
click node_regression "https://github.com/jpandersen61/machine-learning/blob/main/Linear_Regression.ipynb"
click node_startups "https://github.com/jpandersen61/machine-learning/blob/main/50StartUps.ipynb"
click node_startupdata "https://github.com/jpandersen61/machine-learning/blob/main/50StartUps_Load_Data.ipynb"
click node_startupcsv "https://github.com/jpandersen61/machine-learning/blob/main/50_Startups.csv"
click node_housing "https://github.com/jpandersen61/machine-learning/blob/main/Housing_1_Notebook.ipynb"
click node_life "https://github.com/jpandersen61/machine-learning/blob/main/Linear_Regression_Lifeexpectancy.ipynb"
click node_lifecsv "https://github.com/jpandersen61/machine-learning/blob/main/Life%20Expectancy%20Data%20(WHO).csv"
click node_diabetes "https://github.com/jpandersen61/machine-learning/blob/main/Classification_Diagnosing_Diabetes.ipynb"
click node_diabetescsv "https://github.com/jpandersen61/machine-learning/blob/main/diabetes.csv"
click node_mnist "https://github.com/jpandersen61/machine-learning/blob/main/Classfication_MNIST_Exercise.ipynb"
click node_fashion "https://github.com/jpandersen61/machine-learning/blob/main/Fashion_classification_neural_nets_with_keras_start.ipynb"
click node_wine "https://github.com/jpandersen61/machine-learning/blob/main/Mandatory_assignment_Wine_Quality.ipynb"
click node_winecsv "https://github.com/jpandersen61/machine-learning/blob/main/WineQT.csv"
click node_svm "https://github.com/jpandersen61/machine-learning/blob/main/SupportVectorMachinesIris.ipynb"
click node_cluster "https://github.com/jpandersen61/machine-learning/blob/main/Unsupervised_learning_clustering_Setup.ipynb"
click node_mallcsv "https://github.com/jpandersen61/machine-learning/blob/main/Mall_Customers.csv"
click node_chat "https://github.com/jpandersen61/machine-learning/blob/main/ChatGPT_Chat_Sequence.ipynb"
click node_structured "https://github.com/jpandersen61/machine-learning/blob/main/ChatGPT_Structured_Output.ipynb"
click node_news "https://github.com/jpandersen61/machine-learning/blob/main/ChatGPT_News_Generator.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_python1,node_python2,node_initial toneBlue
class node_regression,node_startups,node_startupdata,node_startupcsv,node_housing,node_life,node_lifecsv,node_diabetes,node_diabetescsv,node_mnist,node_fashion,node_wine,node_winecsv,node_svm toneAmber
class node_cluster,node_mallcsv toneMint
class node_chat,node_structured,node_news toneRose
class node_learner toneIndigo
```

## Components

### Python Foundations

- [Python Basics 1](https://github.com/jpandersen61/machine-learning/blob/main/Python_Basic_1.ipynb): `Python_Basic_1.ipynb`
- [Python Basics 2](https://github.com/jpandersen61/machine-learning/blob/main/Python_Basic_2.ipynb): `Python_Basic_2.ipynb`
- [Python Exercise](https://github.com/jpandersen61/machine-learning/blob/main/Initial_Python_Execise.ipynb): `Initial_Python_Execise.ipynb`

### Supervised Learning

- [Linear Regression](https://github.com/jpandersen61/machine-learning/blob/main/Linear_Regression.ipynb): `Linear_Regression.ipynb`
- [Startup Regression](https://github.com/jpandersen61/machine-learning/blob/main/50StartUps.ipynb): `50StartUps.ipynb`
- [Startup Data Loading](https://github.com/jpandersen61/machine-learning/blob/main/50StartUps_Load_Data.ipynb): `50StartUps_Load_Data.ipynb`
- [Startup Data](https://github.com/jpandersen61/machine-learning/blob/main/50_Startups.csv): `50_Startups.csv`
- [Housing Exercises](https://github.com/jpandersen61/machine-learning/blob/main/Housing_1_Notebook.ipynb): `Housing_1_Notebook.ipynb`
- [Life Expectancy Regression](https://github.com/jpandersen61/machine-learning/blob/main/Linear_Regression_Lifeexpectancy.ipynb): `Linear_Regression_Lifeexpectancy.ipynb`
- [Life Expectancy Data](https://github.com/jpandersen61/machine-learning/blob/main/Life%20Expectancy%20Data%20(WHO).csv): `Life Expectancy Data (WHO).csv`
- [Diabetes Classification](https://github.com/jpandersen61/machine-learning/blob/main/Classification_Diagnosing_Diabetes.ipynb): `Classification_Diagnosing_Diabetes.ipynb`
- [Diabetes Data](https://github.com/jpandersen61/machine-learning/blob/main/diabetes.csv): `diabetes.csv`
- [MNIST Classification](https://github.com/jpandersen61/machine-learning/blob/main/Classfication_MNIST_Exercise.ipynb): `Classfication_MNIST_Exercise.ipynb`
- [Fashion Classification](https://github.com/jpandersen61/machine-learning/blob/main/Fashion_classification_neural_nets_with_keras_start.ipynb): `Fashion_classification_neural_nets_with_keras_start.ipynb`
- [Wine Quality](https://github.com/jpandersen61/machine-learning/blob/main/Mandatory_assignment_Wine_Quality.ipynb): `Mandatory_assignment_Wine_Quality.ipynb`
- [Wine Quality Data](https://github.com/jpandersen61/machine-learning/blob/main/WineQT.csv): `WineQT.csv`
- [Iris SVM](https://github.com/jpandersen61/machine-learning/blob/main/SupportVectorMachinesIris.ipynb): `SupportVectorMachinesIris.ipynb`

### Unsupervised Learning

- [Clustering Lessons](https://github.com/jpandersen61/machine-learning/blob/main/Unsupervised_learning_clustering_Setup.ipynb): `Unsupervised_learning_clustering_Setup.ipynb`
- [Customer Data](https://github.com/jpandersen61/machine-learning/blob/main/Mall_Customers.csv): `Mall_Customers.csv`

### Generative AI

- [Chat Sequence](https://github.com/jpandersen61/machine-learning/blob/main/ChatGPT_Chat_Sequence.ipynb): `ChatGPT_Chat_Sequence.ipynb`
- [Structured Output](https://github.com/jpandersen61/machine-learning/blob/main/ChatGPT_Structured_Output.ipynb): `ChatGPT_Structured_Output.ipynb`
- [News Generator](https://github.com/jpandersen61/machine-learning/blob/main/ChatGPT_News_Generator.ipynb): `ChatGPT_News_Generator.ipynb`

### Other components

- **Learner**

## Connections

- Learner → Python Basics 1: opens
- Learner → Python Basics 2: opens
- Learner → Python Exercise: opens
- Learner → Linear Regression: opens
- Learner → Startup Regression: opens
- Learner → Startup Data Loading: opens
- Learner → Housing Exercises: opens
- Learner → Life Expectancy Regression: opens
- Learner → Diabetes Classification: opens
- Learner → MNIST Classification: opens
- Learner → Fashion Classification: opens
- Learner → Wine Quality: opens
- Learner → Iris SVM: opens
- Learner → Clustering Lessons: opens
- Learner → Chat Sequence: opens
- Learner → Structured Output: opens
- Learner → News Generator: opens
- Startup Data Loading → Startup Data: likely loads
- Life Expectancy Regression → Life Expectancy Data: likely uses
- Diabetes Classification → Diabetes Data: likely uses
- Wine Quality → Wine Quality Data: likely uses
- Clustering Lessons → Customer Data: likely uses