AI Career Roadmap 2026 — Step-by-Step Guide for Beginners | AI skills 2026

AI Career Roadmap 2026 — Step-by-Step Guide for Beginners

Short, actionable roadmap to start a career in AI — skills, tools, projects & next steps.

1. Foundations — Math & Programming

Start with Python, basic statistics, and linear algebra. Learn Excel for quick data checks and SQL for querying datasets.

2. Core Data Skills

• Python libraries: NumPy, Pandas, Matplotlib
• Data cleaning, exploratory data analysis (EDA)
• Visualization tools and dashboards

3. Machine Learning Essentials

Learn supervised and unsupervised algorithms (regression, classification, clustering). Use scikit-learn and practice with real datasets. Build small end-to-end ML projects.

4. Specialize & Build Projects

Choose a track: Data Scientist, Business Analyst, AI Engineer or ML Researcher. Build 3–5 portfolio projects and publish them on GitHub.

5. Web & Deployment (Optional but valuable)

Learn basics of deploying models with Flask/ FastAPI and optionally a frontend framework like Next.js for demoing projects.

6. Job Prep & Growth

• Create a concise resume and LinkedIn profile with project links
• Prepare for interviews: coding + ML system design
• Apply for internships and junior roles; keep learning

Quick Resources

• Python + Pandas tutorials
• Machine Learning courses (practical, project-based)
• SQL practice & GitHub portfolio templates

© Vixeli • Updated 2026 — This guide is introductory. Tailor paths to your goals and keep practicing.

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