# Roadmap

> *(For real AI engineering and research — LLMs are often easier than areas like Reinforcement Learning.)*

***

## 📘 Phase 1: Foundations *(1–3 Months)*

* Master **Python**, **NumPy**, **Pandas**, **Matplotlib**
* Learn **Deep Learning** using **PyTorch** or **TensorFlow**
* Understand basic ML models:
  * Linear Regression
  * Logistic Regression
  * Decision Trees
  * Feedforward Neural Networks
  * Convolutional Neural Networks (CNNs)

***

## 📗 Phase 2: Intermediate *(3–6 Months)*

* Study:
  * **GANs (Generative Adversarial Networks)**
  * **VAEs (Variational Autoencoders)**
  * **Transformers**
  * **Reinforcement Learning Basics**
* Hands-on tools:
  * Build with **Hugging Face Transformers**
  * Create demos with **Gradio**
* Use **Google Colab** or **Kaggle** for training
* Containerize your projects with **Docker**

***

## 📕 Phase 3: Advanced *(6–12 Months)*

* Train small-scale **LLMs** using **Unsloth** or **QLoRA**
* Build multi-step pipelines with:
  * **LangChain**
  * **N8N** for automation
* Explore **distributed training** with:
  * **Kubeflow**
  * **Vertex AI**
* Learn optimization techniques:
  * Model distillation
  * Quantization
  * Efficient attention methods

***

## 📙 Phase 4: Engineering Mastery

* Design inference and training clusters with:
  * **Talos Linux**
  * **Kubernetes (K8s)**
* Integrate **Vector Databases**:
  * Pinecone, Weaviate, Redis, etc.
* Fine-tune and deploy **production-grade LLMs**
* Implement full feedback + monitoring loops:
  * Logging
  * Analytics
  * Agent-based retraining


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