Data- Science Road Map

A Machine Learning and Generative AI Roadmap provides a structured path to develop the essential skills and knowledge required to become an AI professional. It begins with strong prerequisites in mathematics (linear algebra, probability, statistics, calculus), Python programming, and tools like Git, GitHub, and Jupyter Notebook. Next comes core machine learning, focusing on supervised and unsupervised learning, model evaluation, and small projects such as regression and classification. This is followed by deep learning, covering neural networks, CNNs for image data, and RNNs/LSTMs for sequential data using frameworks like TensorFlow or PyTorch. The roadmap then advances to generative AI, emphasizing transformer architectures, attention mechanisms, prompt engineering, and fine-tuning open-source models such as GPT or BERT to build chatbots and image generation tools. Finally, it includes applied skills and deployment, teaching how to deploy models with Streamlit, Gradio, or REST APIs (Flask/FastAPI) and introducing MLOps concepts like versioning, monitoring, and automation—helping learners progress from foundational theory to real-world AI application development.