AI/ML Learning Roadmap: A Practical Journey from Basics to Multimodal Large Models

This article introduces an open learning project for cybersecurity professionals transitioning to AI/ML. It systematically covers machine learning basics, deep learning, NLP & LLM, local model deployment, multimodal AI, and comprehensive projects through six modules, providing a reference path for similar transitioners. The project adopts an "open learning" model, combining theory and practice to help learners build a complete knowledge system.

In today's era of AI technology popularization, many professionals feel confused about transitioning to AI/ML (not knowing where to start, path planning, balancing theory and practice). This project comes from the transition practice of a cybersecurity professional. By publicly documenting the learning process (Learn in Public), it not only precipitates personal knowledge but also provides references for other transitioners in the community.

The project is divided into six progressive modules:

1. Machine Learning Basics: Core concepts of supervised/unsupervised learning, classic algorithms, feature engineering, scikit-learn hands-on practice;
2. Deep Learning: Basics of neural networks, CNN/RNN, PyTorch framework;
3. NLP & LLM: Transformer architecture, attention mechanism, Claude API calls, RAG system construction;
4. Local Model Deployment: Ollama tool, model quantization, agent development, privacy protection;
5. Multimodal AI: Vision-language models, audio processing, multimodal fusion;
6. Comprehensive Project: Build an AI security assistant, integrate all skills to solve practical cybersecurity problems.

Tech Stack: Python3 (programming language), Jupyter Notebook (development environment), scikit-learn/PyTorch (ML/DL frameworks), Claude API+Ollama (LLM), Kaggle+public security datasets; Learning Methodology:

• Practice-driven: Each module includes runnable projects, master concepts through coding;
• Progressive difficulty: From basics to advanced, no gaps in knowledge;
• Open learning: Public progress on GitHub, get community feedback and motivation.

1. Leverage domain advantages: Combine original domain (e.g., author's cybersecurity) for cross-application (AI security assistant) to be more competitive;
2. Systematic learning: A complete roadmap avoids fragmentation and builds deep understanding;
3. Hands-on practice: AI/ML requires practical operation; you can't master it just by watching tutorials;
4. Embrace open-source ecosystem: Use open-source tools (scikit-learn, PyTorch, etc.) to reduce learning costs and make skills more universal.

As of the recording time, Module 1 (Machine Learning Basics) is in progress, and the rest of the modules are "coming soon". Participation methods:

1. Install Python3 and Jupyter;
2. Clone the project repository;
3. Learn in module order;
4. Record study notes and experiment results.

This project is not only learning material but also a community contribution of "transitioners helping transitioners". It shows how to turn personal learning into valuable resources and proves the importance of systematic learning and continuous practice. For transitioners, this is a verified path reference; the key is to start acting and persist.