Learning Paths
Not sure where to start? Study maps give you the order and the time needed for a role or a skill; paths with lessons add the lessons to read. Each one says which it is.
3 paths with lessons and 72 study maps.
All Paths
75 pathsAI Engineer
Become an AI Engineer: LLMs, prompt engineering, RAG, vector databases, embeddings, AI agents, MCP and model integration in production.
Machine Learning Engineer
Become a Machine Learning Engineer starting from the basics: Python for data, mathematics, classical machine learning, neural networks, CNNs, RNNs, generative models, reinforcement learning, AI agents, LLMs and RAG, up to MLOps with Docker and MLflow. 114 articles in 12 modules.
Linux: Fundamentals and Administration
Learn Linux: basic commands, file system, permissions, process management, networking, shell scripting, package management and server administration.
Frontend Developer
Become a complete frontend developer: HTML, CSS, JavaScript, modern frameworks, testing, performance and deployment. Follow the progression from basics to advanced concepts.
Backend Developer
Master backend development: RESTful APIs, relational and NoSQL databases, authentication, caching, message brokers, scalable architectures and web security.
Full Stack Developer
Combine frontend and backend skills to become a full stack developer. Covers the entire development cycle: UI, API, database, deployment and CI/CD.
DevOps Engineer
Learn DevOps practices: Linux, scripting, Docker containers, CI/CD, Infrastructure as Code with Terraform, Kubernetes, monitoring and cloud providers.
Data Engineer
Build scalable data pipelines: ETL/ELT, data warehouses, Apache Spark, Airflow, dbt, Kafka and modern data architectures.
Cyber Security Specialist
Protect systems and networks: networking fundamentals, cryptography, vulnerability analysis, penetration testing, web security and OWASP best practices.
Software Architect
Design robust software architectures: design patterns, microservices, event-driven architecture, DDD, CQRS, system design and architectural decisions.
MLOps Engineer
Bring machine learning to production: ML pipelines, experiment tracking, model serving, monitoring, feature stores and ML lifecycle automation.
QA Engineer
Ensure software quality: manual and automated testing, unit tests, integration tests, E2E with Playwright/Cypress, performance testing and CI/CD.