Complete learning path
This roadmap will guide you through becoming an AI/ML Engineer. You'll master Python, mathematics fundamentals, machine learning algorithms, deep learning, NLP, computer vision, and MLOps. This path is designed for those with basic programming knowledge and can be completed in 12-18 months with dedicated effort (4-5 hours daily).
Prerequisites
Basic Python knowledge
Outcome
AI/ML Engineer Role
Resources
Mostly free resources
Build a strong foundation in Python programming, the primary language for AI/ML.
Master the mathematical foundations essential for understanding ML algorithms.
Learn core ML concepts, algorithms, and how to build predictive models.
Master neural networks, architectures, and deep learning frameworks.
Learn to process and understand human language using ML/DL techniques.
Learn to process and analyze visual data using deep learning.
Learn to deploy, monitor, and maintain ML models in production.
Explore cutting-edge AI topics and choose your specialization area.
Don't skip linear algebra and calculus - they're crucial for understanding ML.
Participate in Kaggle competitions to gain practical experience.
Stay updated with latest research from arXiv and top conferences.
Theory alone isn't enough - build and deploy real ML applications.
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Reviewed by
Sproutern Editorial Team
Career editors and quality reviewers working from our public editorial policy
Last reviewed
March 6, 2026
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