Complete 2026 Learning Path

AI/ML Learning Roadmap

Created by Debasish Maji — Senior AI Engineer with 8+ years building production ML systems at Atlassian & PhonePe

The exact learning path used by 2,400+ students to break into AI. Step-by-step guide from Python basics to production ML systems.

2,400+ students followed
850+ job placements
67% avg salary increase
28-38 Weeks Total
15+ Real Projects
Job-Ready Skills
If you study 15 hrs/week:~7 months to completion

Most working professionals complete in 6-9 months

Who Is This Roadmap For?

Career Changers

Transitioning from other fields like software development, finance, or academia

Fresh Graduates

CS/Engineering students looking to specialize in AI/ML

Working Professionals

Developers and data analysts wanting to upskill while employed

Self-Taught Learners

Anyone with dedication and basic computer literacy

Stage 1

Foundation

4-6 weeks

Python Programming

Variables, loops, functions, OOP, file handling

Mathematics Basics

Linear algebra, calculus, probability, statistics

Data Manipulation

NumPy, Pandas, data cleaning, preprocessing

Data Visualization

Matplotlib, Seaborn, Plotly, exploratory analysis

Hands-on Projects:

Stock Portfolio AnalyzerE-commerce A/B Testing PlatformCOVID-19 Data Tracker
Stage 2

Machine Learning Fundamentals

6-8 weeks

Supervised Learning

Linear/logistic regression, decision trees, SVM, KNN

Unsupervised Learning

K-means, hierarchical clustering, PCA, t-SNE

Model Evaluation

Cross-validation, metrics, bias-variance tradeoff

Feature Engineering

Feature selection, encoding, scaling, feature creation

Hands-on Projects:

Real Estate Price Prediction APIE-commerce Customer SegmentationReal-Time Fraud Detection Engine
Stage 3

Deep Learning

8-10 weeks

Neural Networks

Perceptrons, activation functions, backpropagation

CNNs

Convolutional layers, pooling, image classification

RNNs & LSTMs

Sequence modeling, time series, text processing

Transformers

Attention mechanism, BERT, GPT architectures

Hands-on Projects:

Medical X-Ray Diagnosis SystemTwitter/X Sentiment DashboardAI Content GeneratorYOLO Object Detection

Halfway there! At this point, many learners benefit from structured guidance. See how our program accelerates Stages 4-6 →

Stage 4

Specialization

6-8 weeks

Computer Vision

Object detection, segmentation, GANs, face recognition

NLP

Named entity recognition, question answering, summarization

Generative AI

Stable Diffusion, LLMs, prompt engineering, fine-tuning

Reinforcement Learning

Q-learning, policy gradients, actor-critic methods

Hands-on Projects:

Enterprise RAG ChatbotAI Image GeneratorNetflix-Style RecommenderAutonomous AI Agent
Stage 5

Production & Deployment

4-6 weeks

MLOps

ML pipelines, experiment tracking, model versioning

Model Deployment

REST APIs, Docker, Kubernetes, cloud platforms

Monitoring

Model drift, performance monitoring, A/B testing

Scalability

Distributed training, model optimization, inference

Hands-on Projects:

Production ML API with CI/CDKubernetes-Deployed AI ServiceFull-Stack AI Application
Stage 6

AI-Powered Engineering

1 week

Claude Code

Multi-file editing, codebase understanding, complex debugging

GitHub Copilot

Inline completions, chat mode, Copilot Workspace

Cursor & AI IDEs

Next-generation AI-native development environments

AI Workflows

TDD with AI, code review, refactoring, auto-documentation

Hands-on Projects:

Full-Stack App Built with AI ToolsOpen Source Contribution via Claude Code

Start Your AI Journey Today

Follow this roadmap with expert guidance, hands-on projects, and career support. Join our structured program and accelerate your learning.

Join 2,400+ Students Learning AIStart Free: Python Foundations

Success Stories from This Roadmap

Real students who followed this exact roadmap and transformed their careers

"This roadmap gave me the clarity I needed. Within 8 months of following it, I transitioned from web development to ML engineering at Google."

PS

Priya Sharma

ML Engineer @ Google

Previously:Software Developer→$185K

"The structured approach and real projects made all the difference. I went from basic Python to leading an AI team in 14 months."

RV

Rahul Verma

AI Lead @ Flipkart

Previously:Data Analyst→₹45 LPA

"As a fresh grad with no ML experience, this roadmap was my complete guide. Now I'm building production ML systems at Microsoft."

SC

Sarah Chen

Senior Data Scientist @ Microsoft

Previously:Fresh Graduate→$165K

FEELING OVERWHELMED?

Learn This Roadmap 3x Faster

Our Professional Program teaches all 6 stages in 20 weeks with live mentorship, code reviews, and guaranteed job support. 89% completion rate.

  • Weekly live sessions with industry experts
  • 1:1 mentorship and code reviews
  • Career support until you get hired
See the Full Curriculum

20

weeks structured

850+

graduates hired

Frequently Asked Questions

Answers to common questions about learning AI and following this roadmap

How long does it take to learn AI from scratch?

Following this roadmap with 15-20 hours per week, most learners complete the full curriculum in 7-9 months. If you can dedicate full-time hours (40+/week), you can finish in 4-5 months. The key is consistency rather than speed.

Do I need a computer science degree to follow this roadmap?

No. This roadmap is designed for anyone with basic computer literacy. We start from Python fundamentals and build up. Many successful AI engineers came from non-CS backgrounds including finance, biology, and arts.

What math do I need for machine learning?

Stage 1 covers all the math you need: linear algebra (matrices, vectors), calculus (derivatives, gradients), probability, and statistics. You don't need to be a math expert—you need to understand how these concepts apply to ML algorithms.

Can I learn AI while working full-time?

Absolutely. Most of our successful students learned while working. With 10-15 hours per week (1-2 hours daily + weekends), you can complete this roadmap in 10-12 months. The key is setting a consistent schedule.

What's the best first project for AI beginners?

Start with the 'Stock Portfolio Analyzer' in Stage 1. It uses real data, teaches data manipulation, and produces something useful. Avoid starting with complex deep learning—master the fundamentals first.

How is this roadmap different from free YouTube tutorials?

This roadmap provides structured progression, not random videos. Each stage builds on the previous one, with carefully sequenced projects that mirror real-world work. It's based on 8+ years of production AI experience, not theoretical knowledge.

What job roles can I apply for after completing this roadmap?

After completing all 6 stages, you'll be qualified for: ML Engineer, Data Scientist, AI Engineer, NLP Engineer, Computer Vision Engineer, MLOps Engineer, and AI Product roles. Most of our graduates land jobs within 3 months of completion.

Should I learn TensorFlow or PyTorch?

We recommend starting with PyTorch—it's more intuitive and dominates in research and startups. Stage 3 covers PyTorch in depth. Once you know one framework well, learning the other takes just 1-2 weeks.

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A roadmap is just theory without execution. Get live mentorship at every step.

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Taught by Debasish Maji — Senior AI Engineer · Ex-Atlassian (Rovo Agent) · Ex-PhonePe (550M+ users)

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