Last updated: May 2026
Our amazing step-by-step roadmap to go from complete beginner to building real machine learning projects
Become an ML Engineer
in 90 Days
Learn Machine Learning step by step with our Free 90-day zero-to-hero roadmap. From Python and Math to real ML projects in GitHub and portfolio building.
You will get an easy to understand theoretical base, hands-on guides for coding, links to very special videos, articles, and papers, and a lot more!
The 90-Day Roadmap
3 Phases • 90 Days • One Goal: Become an ML Engineer
PHASE 1 · Days 1–30
Foundations
Learn the fundamentals you need to understand machine learning.
Variables, loops, functions, data structures
Data manipulation and analysis
Linear algebra, probability, distributions
PHASE 2 · Days 31–60
Core ML
Understand the key machine learning algorithms and how they work.
Regression and classification basics
KNN, Trees, Random Forest, SVM
Metrics, validation, tuning
PHASE 3 · Days 61–90
Projects & Portfolio
Build projects and create a portfolio that gets attention.
Build practical ML projects
Feature engineering and ensembles
Portfolio, blog, deploy a model
Stay on track. Get the Roadmap.
Download the 90-Day Roadmap PDF + get weekly tips and resources to help you succeed
Our Free 90-Day Machine Learning Roadmap will take you from Zero-to job ready in just 90 days building your foundational knowledge, linking you to great additional resources, and starting your own personal ML portfolio.
90 Days: Step by step plan
3+ Projects: Build e2e Projects
Free Resources: Curated just for you
Who is this Roadmap for?
This roadmap is designed for complete beginners who want to go from zero to real Machine
Learning projects.
No previous coding experience. No advanced math. No prior experience. Just curiosity, consistency, and the will to learn. This roadmap is perfect if you are:
- A complete beginner: you’re new to ML and want a clear, step by step path.
- A Student or Self Learner: you prefer learning on your own and building real skills.
- A Career Changer: you want to break into tech or move into ML.
- An Aspiring Data Scientist: you want to build projects and a strong portfolio.
- A Builder at Heart: you learn best by doing and love solving problems.
Frequently Asked Questions
How long does it really take to learn machine learning?
With consistent daily effort of 1–2 hours, 90 days is enough to build a strong foundation, complete real projects, and start applying for entry-level ML roles. Going deeper into specializations like deep learning, NLP, or MLOps takes additional months and love but this roadmap gets you to job-ready basics fast.
Is this roadmap really free?
Yes. The 90-day roadmap PDF is completely free. You’ll also get weekly tips and curated resources by email: no payment, no credit card.
Do I need a math background?
No advanced math degree required. The roadmap covers the specific linear algebra, probability, and statistics concepts you actually need for ML, taught in plain language with practical examples. High school math and some curiosity is enough to start.
Do I need to know Python first?
No. Phase 1 (Days 1–30) starts from absolute Python basics: variables, loops, functions, and data structures, before moving to NumPy and Pandas. If you already know Python, you can move through Phase 1 faster.
What tools or laptop do I need?
Any laptop that can run a browser works. Most of the roadmap uses free tools: Python, Jupyter notebooks, Google Colab (free GPU access), scikit-learn, and GitHub. No paid software required.
Can I really get a job after 90 days?
The roadmap gets you portfolio-ready with real ML projects and the foundational knowledge employers expect. Landing a job also depends on practice, networking, and interview prep, but completing this roadmap puts you in a strong position to apply for junior ML, data science, and ML engineer roles.
What comes after the 90 days?
After completing the roadmap, you can specialize in areas like deep learning, NLP, computer vision, or MLOps. Check our machine learning books and courses pages for curated next steps.
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