Surendar

Step-1: Learn Programming Fundamentals (Prefer Python) Step-2: Master Mathematics for AI (Linear Algebra, Probability, Statistics) Step-3: Understand Machine Learning Basics (Supervised, Unsupervised Learning) Step-4: Learn Data Handling (NumPy, Pandas, Data Cleaning) Step-5: Master Deep Learning Concepts (Neural Networks, CNNs, RNNs) Step-6: Learn AI Frameworks (TensorFlow, PyTorch, Scikit-learn) Step-7: Understand NLP & Computer Vision Basics Step-8: Learn Model Deployment (APIs, Docker, FastAPI) Step-9: Understand MLOps (Model Versioning, Monitoring, CI/CD for ML) Step-10: Learn Vector Databases & LLM Tools (LangChain, RAG, Embeddings) Step-11: Build Real AI Projects and Practice Constantly Step-12: Stay updated on AI Research and Best Practices Congratulations, you're an AI Engineer!