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.

[ajay-dhangar]

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

I'll set up the documentation folder structure and create some default content. I'll improve the documentation based on feedback from experts.