3–6 Month Generative AI Learning Plan

 Based on your background as a Java developer and your interest in transitioning into Generative AI, here's a personalized 3–6 month learning plan along with starter projects tailored to your goals: text generation, image generation, and code generation.

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🗓️ Personalized 3–6 Month Generative AI Learning Plan

🎯 Goals:

1. Master core ML/DL concepts for GenAI

2. Gain hands-on experience with LLMs and image models

3. Build GenAI-powered microservices (integrated with Java if needed)

4. Deploy and showcase real-world GenAI projects

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📆 Month-by-Month Plan

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✅ Month 1: Foundation — Python & ML Basics

📚 Topics

• Python for ML (if you're not already fluent)

• NumPy, Pandas, Matplotlib

• scikit-learn basics: linear regression, decision trees, clustering

• Git & GitHub

🔧 Tools

• Jupyter Notebook / VSCode

• Google Colab

• GitHub

🛠️ Starter Project

"ML-Powered Resume Analyzer"
Build a tool to analyze and score resumes using keyword matching + ML classification.

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✅ Month 2: Deep Learning + NLP Basics

📚 Topics

• Neural Networks, Activation Functions, Loss Functions

• Deep Learning with TensorFlow or PyTorch

• NLP Basics: tokenization, embeddings, attention

• Hugging Face Transformers

🔧 Tools

• TensorFlow or PyTorch

• Hugging Face Hub

• Google Colab

🛠️ Starter Project

"Text Summarizer or Question Answering Bot"
Use BERT or T5 to summarize content or answer user questions.

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✅ Month 3: Language Models (LLMs) + Prompt Engineering

📚 Topics

• GPT architecture

• Prompt engineering (zero-shot, few-shot)

• Fine-tuning small LLMs (like DistilGPT2)

• Intro to LangChain or LlamaIndex

🔧 Tools

• OpenAI API or Hugging Face Transformers

• LangChain

• Streamlit or Gradio

🛠️ Starter Project

"ChatGPT Clone for Customer Support"
Custom prompt-based chatbot trained with company FAQs using OpenAI or LLaMA.

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✅ Month 4: Image Generation & Code Gen

📚 Topics

• GANs, VAEs (high-level understanding)

• Stable Diffusion, DALL·E 2

• Code generation using Codex or Code LLaMA

🔧 Tools

• Diffusers (Hugging Face)

• Replicate or OpenAI APIs

• GitHub Copilot / Code LLaMA

🛠️ Starter Projects

• "AI Image Prompt Generator" – Generates images from creative prompts

• "Code Generator Assistant" – Converts natural language → Java/Python snippets

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✅ Month 5: GenAI APIs + Java Integration

📚 Topics

• REST APIs with Flask/FastAPI

• Consuming Python APIs from Spring Boot

• Vector DBs (FAISS, Chroma, Pinecone)

• RAG (Retrieval-Augmented Generation)

🔧 Tools

• FastAPI + Uvicorn

• Spring Boot + RestTemplate or Feign

• LangChain + ChromaDB

🛠️ Starter Project

"Java + GenAI Search Assistant"
Ask questions in a Spring Boot app → Python API does RAG using documents.

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✅ Month 6: Model Deployment, MLOps & Portfolio

📚 Topics

• Dockerizing AI apps

• Cloud deployment (AWS/GCP/Azure or Hugging Face Spaces)

• Model versioning, logging, evaluation (Weights & Biases)

• Writing technical blog posts (LinkedIn/Medium)

🔧 Tools

• Docker, GitHub Actions

• AWS EC2/SageMaker or Hugging Face Spaces

• Weights & Biases

🛠️ Final Capstone Project

"Generative AI Microservice Platform"
A full-stack GenAI app (React frontend + Spring Boot backend + Python GenAI APIs) that:

• Generates text/code/images

• Integrates search using LLM + RAG

• Deploys on the cloud

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🧰 Bonus Tools & Concepts (Explore Anytime)

Tool / Concept	Why it's useful
LangChain	Orchestrates LLM workflows
Pinecone / FAISS	Store & search embeddings
Streamlit / Gradio	Build quick AI demos
Docker	Package and deploy your app
OpenAI, Anthropic, Mistral APIs	LLM-as-a-service

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📘 Learning Resources

Resource	Type
Hugging Face Course	Free tutorials
Full Stack Deep Learning	End-to-end ML/AI
fast.ai	Practical deep learning
OpenAI Cookbook	Prompt engineering + API

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🏁 Your Next Steps

1. Pick a project from Month 1 or 2

2. Set up GitHub repos

3. Start sharing progress on LinkedIn

4. Ask anytime — I can help with code, design, or debugging!