Machine Learning Tutorials Structure Overview & Contribution Guide (CodeHarborHub)

[ajay-dhangar]

Welcome, contributors 👋.

This discussion provides a clear overview of our tutorial repository structure, including folders, file paths, and how content is organized.

The goal is to help contributors:

• Understand where to add new tutorials
• Follow a consistent structure
• Avoid confusion while contributing
• Scale the project cleanly as it grows

This repository follows a modular, topic-based structure optimized for Docusaurus (MDX + React).

---

🗂️ High-Level Folder Structure

/tutorials
│
├── web-development/
│   ├── html/
│   ├── css/
│   ├── javascript/
│
├── machine-learning/
│   ├── introduction.mdx
│   ├── fundamentals/
│   ├── algorithms/
│   ├── projects/
│
├── data-science/
│   ├── python/
│   ├── statistics/
│   ├── visualization/
│
└── assets/
    ├── images/
    ├── diagrams/

---

Visual Overview (Mermaid)

flowchart TD
  A[Tutorials Root] --> B[Web Development]
  A --> C[Machine Learning]
  A --> D[Data Science]
  A --> E[Assets]

  B --> B1[HTML]
  B --> B2[CSS]
  B --> B3[JavaScript]

  C --> C1[Introduction]
  C --> C2[Fundamentals]
  C --> C3[Algorithms]
  C --> C4[Projects]

  D --> D1[Python]
  D --> D2[Statistics]
  D --> D3[Visualization]

Loading

---

Machine Learning Tutorials Structure

/tutorial/machine-learning
|
|── introduction
|    |── what-is-ml.mdx
|    |── role-of-ml-engineer.mdx
|    |── ml-engineer-vs-ai-engineer.mdx
|    |── skills-and-responsibilities.mdx
|    |── ml-lifecycle.mdx
|
|── mathematics-for-ml
|    |
|    |── linear-algebra
|    |    |── scalars.mdx
|    |    |── vectors.mdx
|    |    |── matrices.mdx
|    |    |── tensors.mdx
|    |    |── matrix-operations.mdx
|    |    |── determinants.mdx
|    |    |── inverse-of-matrix.mdx
|    |    |── eigenvalues-and-eigenvectors.mdx
|    |    |── svd.mdx
|    |    |── diagonalization.mdx
|    |
|    |── calculus
|    |    |── derivatives.mdx
|    |    |── partial-derivatives.mdx
|    |    |── chain-rule.mdx
|    |    |── gradients.mdx
|    |    |── jacobian.mdx
|    |    |── hessian.mdx
|    |
|    |── discrete-mathematics
|         |── sets-and-relations.mdx
|         |── logic.mdx
|         |── combinatorics.mdx
|         |── graphs.mdx
|
|── statistics
|    |── basic-concepts.mdx
|    |── descriptive-statistics.mdx
|    |── data-visualization.mdx
|    |── inferential-statistics.mdx
|
|── probability
|    |── basics-of-probability.mdx
|    |── conditional-probability.mdx
|    |── bayes-theorem.mdx
|    |── random-variables.mdx
|    |── pdf-pmf.mdx
|    |── probability-distributions.mdx
|    |    |── normal.mdx
|    |    |── binomial.mdx
|    |    |── poisson.mdx
|    |    |── uniform.mdx
|
|── programming-fundamentals
|    |── python.mdx
|    |
|    |── basic-syntax
|    |    |── variables-and-data-types.mdx
|    |    |── data-structures.mdx
|    |    |── loops.mdx
|    |    |── conditionals.mdx
|    |    |── exceptions.mdx
|    |    |── functions.mdx
|    |
|    |── object-oriented-programming.mdx
|    |
|    |── essential-libraries
|    |    |── numpy.mdx
|    |    |── pandas.mdx
|    |    |── matplotlib.mdx
|    |    |── seaborn.mdx
|
|── data-engineering-basics
|    |
|    |── data-collection
|    |    |── data-sources.mdx
|    |    |    |── databases-sql-nosql.mdx
|    |    |    |── internet.mdx
|    |    |    |── apis.mdx
|    |    |    |── mobile-apps.mdx
|    |    |    |── iot.mdx
|    |
|    |── data-formats
|    |    |── csv.mdx
|    |    |── excel.mdx
|    |    |── json.mdx
|    |    |── parquet.mdx
|    |    |── xml.mdx
|    |
|    |── data-cleaning-and-preprocessing
|    |    |── handling-missing-data.mdx
|    |    |── feature-engineering.mdx
|    |    |── feature-scaling.mdx
|    |    |── normalization.mdx
|    |    |── dimensionality-reduction.mdx
|    |    |── feature-selection.mdx
|
|── machine-learning-core
|    |── introduction-to-ml.mdx
|    |
|    |── types-of-machine-learning
|    |    |── supervised-learning.mdx
|    |    |── unsupervised-learning.mdx
|    |    |── semi-supervised-learning.mdx
|    |    |── self-supervised-learning.mdx
|    |    |── reinforcement-learning.mdx
|    |
|    |── scikit-learn
|    |    |── data-loading.mdx
|    |    |── text-data.mdx
|    |    |── data-preparation.mdx
|    |    |── model-selection.mdx
|    |    |── hyperparameter-tuning.mdx
|    |    |── predictions.mdx
|    |
|    |── supervised-learning
|    |    |
|    |    |── classification
|    |    |    |── knn.mdx
|    |    |    |── logistic-regression.mdx
|    |    |    |── svm.mdx
|    |    |    |── decision-trees.mdx
|    |    |    |── random-forest.mdx
|    |    |    |── gradient-boosting.mdx
|    |
|    |    |── regression
|    |         |── linear-regression.mdx
|    |         |── polynomial-regression.mdx
|    |         |── lasso.mdx
|    |         |── ridge.mdx
|    |         |── elastic-net.mdx
|
|    |── unsupervised-learning
|    |    |── clustering
|    |    |    |── kmeans.mdx
|    |    |    |── hierarchical.mdx
|    |    |    |── dbscan.mdx
|    |    |    |── gaussian-mixtures.mdx
|    |
|    |    |── dimensionality-reduction
|    |         |── pca.mdx
|    |         |── autoencoders.mdx
|
|    |── reinforcement-learning
|    |    |── q-learning.mdx
|    |    |── policy-gradients.mdx
|    |    |── actor-critic.mdx
|    |    |── deep-q-networks.mdx
|
|    |── model-evaluation
|    |    |── why-evaluation-matters.mdx
|    |    |── metrics
|    |    |    |── accuracy.mdx
|    |    |    |── precision.mdx
|    |    |    |── recall.mdx
|    |    |    |── f1-score.mdx
|    |    |    |── roc-auc.mdx
|    |    |    |── log-loss.mdx
|    |    |    |── confusion-matrix.mdx
|    |
|    |    |── validation-techniques
|    |         |── train-test-split.mdx
|    |         |── k-fold-cross-validation.mdx
|    |         |── loocv.mdx
|
|
|── deep-learning
|    |
|    |── neural-network-basics
|    |    |── perceptron.mdx
|    |    |── multi-layer-perceptron.mdx
|    |    |── forward-propagation.mdx
|    |    |── backpropagation.mdx
|    |    |── activation-functions.mdx
|    |    |── loss-functions.mdx
|    |
|    |── deep-learning-libraries
|    |    |── tensorflow.mdx
|    |    |── keras.mdx
|    |    |── pytorch.mdx
|
|    |── cnn
|    |    |── convolution.mdx
|    |    |── pooling.mdx
|    |    |── padding.mdx
|    |    |── strides.mdx
|    |
|    |── cnn-applications
|    |    |── image-classification.mdx
|    |    |── image-segmentation.mdx
|    |    |── video-recognition.mdx
|    |    |── recommendation-systems.mdx
|
|    |── rnn
|    |    |── rnn-basics.mdx
|    |    |── gru.mdx
|    |    |── lstm.mdx
|    |
|    |── attention-mechanisms
|    |    |── self-attention.mdx
|    |    |── transformers.mdx
|    |    |── multi-head-attention.mdx
|
|    |── autoencoders.mdx
|    |── gans.mdx
|
|
|── advanced-ml-topics
|    |
|    |── natural-language-processing
|    |    |── tokenization.mdx
|    |    |── stemming.mdx
|    |    |── lemmatization.mdx
|    |    |── embeddings.mdx
|    |    |── attention-models.mdx
|
|    |── explainable-ai
|    |    |── xai-basics.mdx
|    |    |── lime-shap.mdx
|
|    |── mlops
|    |    |── model-deployment.mdx
|    |    |── monitoring.mdx
|    |    |── ci-cd.mdx
|    |    |── data-versioning.mdx
|    |    |── reproducibility.mdx
|
|    |── ai-agents
|    |── ai-engineer-roadmap.mdx
|
|── projects-and-case-studies
|    |── beginner-projects.mdx
|    |── intermediate-projects.mdx
|    |── advanced-projects.mdx
|    |── industry-case-studies.mdx

Path Example

/tutorial/machine-learning/algorithms/linear-regression.mdx

---

MDX File Standards

Each tutorial file must include:

---
title: Page Title
sidebar_label: Sidebar Name
description: Short SEO-friendly description
tags: [machine-learning, beginners]
---

Supported Content:

• Mermaid diagrams
• Tabs & multi-language code blocks
• KaTeX math equations
• Admonitions (tip, note, warning, etc.)

---

Contribution Flow

sequenceDiagram
  Contributor->>Repo: Fork Repository
  Contributor->>Repo: Create Feature Branch
  Contributor->>Docs: Add / Edit MDX File
  Contributor->>Repo: Commit Changes
  Contributor->>Repo: Open Pull Request
  Maintainer->>Repo: Review & Merge

Loading

---

Contribution Guidelines

• Follow existing folder naming conventions
• Use clear, beginner-friendly language
• Prefer diagrams for explanations
• Avoid duplicate topics
• Keep one concept per file
• Test MDX syntax locally (if possible)

---

Why This Structure?

• Scalable for thousands of tutorials
• Easy navigation for learners
• Clean contributor workflow
• SEO + documentation friendly
• Consistent across all tech domains

---

💬 Need Help?

If you are unsure:

• Where to place a file
• How to structure content
• How to use Mermaid/MDX

👉 Start a discussion or comment here.

We’re building CodeHarborHub together.

Happy contributing 💙

[GanLin-06]

“Hi folks, I'm learning Python dictionaries and just got to dictionary comprehensions. I have a list of fruits like fruits = ["apple", "banana", "orange"] — I want to make a dict where keys are the fruit names and values are their lengths. Is this correct: fruit_len_dict = {fruit: len(fruit) for fruit in fruits}? Also, can this be written in a regular loop way for comparison? Thanks!”

[GanLin-06]

Hi folks, I’m a Python beginner diving deeper into dictionary comprehensions! I have a list of fruits with duplicates: fruits = ["apple", "banana", "apple", "orange", "banana"]. I want to make a dictionary where keys are unique fruit names, and values are the number of times each fruit appears in the list.
I tried writing this: fruit_count = {fruit: fruits.count(fruit) for fruit in fruits}, but when I print it, the keys still repeat (and it feels like this code is inefficient). Is there a correct way to do this with a dictionary comprehension? Also, could someone explain why my first attempt doesn’t automatically remove duplicate keys?
Thanks so much for your help!