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

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