Proposed Documentation Folder Structure

[ajay-dhangar]

docs/
├── README.md                             # Overview of the documentation structure
├── getting-started/
│   ├── introduction.md                   # Introduction to AIBuddies and AI
│   ├── prerequisites.md                  # Required skills/knowledge for AI learning
│   ├── setting-up-environment.md         # Guide to setting up the development environment
│   └── tools-and-libraries.md            # Overview of important tools (TensorFlow, PyTorch, etc.)
├── ai-fundamentals/
│   ├── what-is-ai.md                     # Basics of AI
│   ├── history-of-ai.md                  # Evolution of AI over time
│   ├── types-of-ai/
│   │   ├── narrow-ai.md                  # Narrow AI explained
│   │   ├── general-ai.md                 # General AI concepts
│   │   └── super-ai.md                   # Future of AI and Super AI
│   ├── machine-learning-vs-deep-learning.md # Comparison between ML and DL
│   └── key-concepts.md                   # Important AI concepts like algorithms, data, models, etc.
├── machine-learning/
│   ├── introduction.md                   # Overview of machine learning
│   ├── supervised-learning/
│   │   ├── introduction.md               # What is supervised learning?
│   │   ├── regression.md                 # Regression techniques and algorithms
│   │   └── classification.md             # Classification techniques and algorithms
│   ├── unsupervised-learning/
│   │   ├── introduction.md               # What is unsupervised learning?
│   │   ├── clustering.md                 # Clustering techniques and algorithms
│   │   └── dimensionality-reduction.md   # Techniques for dimensionality reduction
│   ├── reinforcement-learning/
│   │   ├── introduction.md               # Introduction to reinforcement learning
│   │   ├── q-learning.md                 # Basic Q-learning techniques
│   │   └── deep-q-networks.md            # Advanced techniques in reinforcement learning
│   └── algorithms-and-techniques.md      # Key ML algorithms and techniques
├── deep-learning/
│   ├── introduction.md                   # Overview of deep learning
│   ├── neural-networks/
│   │   ├── introduction.md               # Basics of neural networks
│   │   ├── feedforward-networks.md       # Feedforward neural networks explained
│   │   └── backpropagation.md            # How backpropagation works
│   ├── convolutional-neural-networks.md  # CNNs and their applications
│   ├── recurrent-neural-networks.md      # RNNs and sequence-based learning
│   ├── transformers.md                   # Modern architectures for deep learning
│   └── optimization-techniques.md        # Training and optimization methods
├── natural-language-processing/
│   ├── introduction.md                   # Overview of NLP
│   ├── text-preprocessing.md             # Text preprocessing techniques
│   ├── sentiment-analysis.md             # Sentiment analysis explained
│   ├── language-models/
│   │   ├── introduction.md               # Overview of language models
│   │   ├── transformers.md               # Transformer models in NLP
│   │   └── word-embeddings.md            # Techniques for word embeddings
│   └── applications.md                   # Real-world applications of NLP
├── ai-ethics-and-safety/
│   ├── introduction.md                   # Overview of AI ethics
│   ├── responsible-ai.md                 # Developing AI responsibly
│   ├── bias-and-fairness.md              # Addressing bias and ensuring fairness
│   ├── privacy.md                        # Protecting user privacy in AI
│   └── ai-safety.md                      # Best practices for AI safety
├── tools-and-frameworks/
│   ├── introduction.md                   # Overview of popular AI tools
│   ├── tensorflow.md                     # TensorFlow library guide
│   ├── pytorch.md                        # PyTorch library guide
│   ├── scikit-learn.md                   # Scikit-learn for machine learning
│   └── jupyter-notebooks.md              # Using Jupyter for data science
├── projects/
│   ├── index.md                          # Overview of AI project ideas
│   ├── beginner-projects.md              # AI projects for beginners
│   ├── intermediate-projects.md          # AI projects for intermediate learners
│   ├── advanced-projects.md              # Challenging AI projects for experts
│   └── real-world-case-studies.md        # Real-world AI use cases and projects
└── style-guide.md                        # Documentation style guide for contributors

Explanation of the Folder Structure

1. getting-started/: Provides a smooth entry point for newcomers to learn about the project, prerequisites, and tools setup.
2. ai-fundamentals/: Covers fundamental AI concepts to build a strong base, starting from the basics to more nuanced topics.
3. machine-learning/ and deep-learning/: These folders separately handle ML and DL topics, giving each the depth it requires.
4. natural-language-processing/: Focuses on NLP, covering essential techniques and real-world applications.
5. ai-ethics-and-safety/: A dedicated section on ethical considerations and safety practices in AI development.
6. tools-and-frameworks/: Helps users get familiar with essential tools and libraries used in AI development.
7. projects/: Provides project ideas for practical learning, categorized by skill level.
8. style-guide.md: Ensures consistent formatting and content style across the documentation.

[Brijeshthummar02]

@ajay-dhangar I would like to take the initiative to work on it. Assign me the task.

[Brijeshthummar02]

so i only need to make it structured right? as show by you.