AI Learning Path

Welcome!

This repository is a structured collection of my journey in AI, Machine Learning, and Deep Learning.

My goal is to create a comprehensive AI learning resource, complete with books, research papers, projects, and hands-on implementations I have completed through the years. Whether you're just getting started or getting into advanced areas like transformers and reinforcement learning, this repository should have something for you!

📂 Repo Structure

AI-Learning-Path/
├── 00_Resources/                     # Core AI learning resources
│   ├── Books/                        # Math, ML, and DL books
│   ├── Research_Papers/              # Key AI and ML research papers
│   └── Other_Materials/              # Cheat sheets, slides, extra materials
├── 01_MLBasics/                      # Machine Learning fundamentals
│   ├── 00_Mathematics/               # Math essentials (linear algebra, calculus)
│   ├── 01_Regression/                # Linear, logistic, multiple regression
│   ├── 02_Classification/            # Decision trees, SVM, Naive Bayes
│   ├── 03_Clustering/                # K-means, hierarchical clustering
│   └── 04_EnsembleMethods/           # Random forests, boosting, bagging
├── 02_FeatureEngineering/            # Feature engineering techniques
│   ├── PCA/                          # Principal Component Analysis
│   ├── simple_methods/               # Standard scaling, encoding
│   └── other_methods/                # Complex and custom feature methods
├── 03_DeepLearning/                  # Neural Networks and DL Architectures
│   ├── 00_MLP/                       # Basic neural networks (MLP)
│   ├── 01_CNN/                       # Convolutional Neural Networks
│   ├── 02_RNN_LSTM/                  # Recurrent Neural Networks, LSTM
│   ├── 03_Transformers/              # Transformers (BERT, GPT, etc.)
│   ├── 04_GAN/                       # Generative Adversarial Networks
│   └── 05_Other_DL_Techniques/       # Autoencoders, hybrid models
├── 04_AdvancedTopics/                # Advanced AI techniques
│   ├── AutoML/                       # Automated Machine Learning
│   ├── HPC_DistributedTraining/      # Distributed training for large models
│   └── ReinforcementLearning/        # Reinforcement learning and agents
├── LICENSE                           # License for the repo
└── README.md                         # This file

What's Inside?

• Resources – A curated collection of essential books, research papers, and cheat sheets.
• Machine Learning Basics – Topics such as regression, classification, clustering, and ensemble techniques.
• Feature Engineering – Techniques to prepare data, including PCA, scaling, and other simple and advanced methods.
• Deep Learning – Neural networks, CNNs, RNNs, transformers, and GANs, with projects for each.
• Advanced AI – Reinforcement learning, distributed training, and AutoML pipelines.

How to Use This Repo

• For Beginners – Start with 01_MLBasics and brush up on foundational math in 00_Mathematics.
• Intermediate Learners – Work on projects under 02_FeatureEngineering and 03_DeepLearning to deepen your knowledge.
• Advanced Practitioners – Head over to 04_AdvancedTopics to implement transformers, RL agents, and large-scale distributed AI models.

Why I Built This Repo

AI is evolving at an incredible pace, and it can be hard to keep track of everything. This repo acts as a structured learning path and project tracker, helping me stay organized while learning.

Want to Collaborate?

Contributions are welcome! If you have ideas for projects or want to add learning resources, feel free to fork this repo, submit a pull request, or open an issue.