Machine Learning & AI — 13 · Month Roadmap

Mathematical foundations through generative AI, research, and MLOps.

00 · Command

• The 13-Month Machine Learning Fortress
• Month-by-Month Execution Plan
• Sprint Calendar
• North Star Artifacts
• The Canonical Week
• KPI Dashboard
• Zoho Background Alignment
• The Thirteen-Month Pitch

01 · Phase 0 Mathematical Foundations

• Phase 0: Mathematical Foundations
• Linear Algebra for Machine Learning
• Calculus and Optimization for Machine Learning
• Probability and Statistics for Machine Learning
• Information Theory for Machine Learning
• Phase 0 Projects: Proving You Know the Math

02 · Phase 1 Classical ML

• Phase 1: Classical Machine Learning (Months 3–4)
• Supervised Learning: Derivations, Code, and Failure Modes
• Unsupervised Learning: Structure Without Labels
• 03 · Model Selection and Evaluation
• 04 · Feature Engineering
• 05 · Classical ML in Production
• 06 · Phase 1 Projects

03 · Phase 2 Deep Learning Core

• Phase 2: Deep Learning Core
• Neural Networks From Scratch
• PyTorch Fundamentals
• 03 — CNNs and Computer Vision
• 04 — Sequence Models
• 05 — Training Dynamics
• 06 — Phase 2 Projects

04 · Phase 3 Modern Architectures

• Phase 3: Modern Architectures (Months 7–8)
• Attention and Transformers
• BERT and the GPT Family
• Vision Transformers
• Diffusion Models: Foundations
• Graph Neural Networks: Foundations
• Phase 3 Projects

05 · Phase 4 Generative Ai Frontier

• Phase 4: Generative AI Frontier (Months 9–10)
• LLM Alignment and Fine-Tuning
• Parameter-Efficient Fine-Tuning (PEFT)
• RAG Systems: Retrieval-Augmented Generation
• LLM Agents: Architecture, Reliability, and Production Reality
• Multi-Modal and Frontier Models
• Phase 4 Projects: Generative AI Frontier

06 · Phase 5 Production and Mlops

• Phase 5: Production & MLOps (Months 11–12)
• 01 — Experiment Tracking & Data Versioning
• 02 — Model Serving & Inference
• 03 — Containerization & Orchestration
• 04 — Monitoring & Data Quality
• 05 — CI/CD for ML
• 06 — Phase 5 Projects

07 · Phase 6 Research and Mastery

• Phase 6 — Research & Mastery (Month 13)
• How to Read ML Papers
• Frontier Papers 2024–2025: The Practitioner’s Reading List
• Open Source Contribution for ML Engineers
• 04 — Developing a Technical Opinion
• 05 — The Staying-Current System
• 06 — Interview & Assessment Mastery
• 07 — The Applied PhD Identity
• 08 — Phase 6 Projects (Month 13 Capstone)

09 · Resources

• 09 — Resources Canon
• 01 — Books Canon
• 02 — Courses Canon
• 03 — Papers Canon
• 04 — Tools and Libraries Canon
• 05 — Datasets Canon

10 · Communities

• 10 — Communities
• 01 — Online Communities
• 02 — Newsletters & Blogs
• 03 — India-Specific ML Communities
• 04 — YouTube Channels & Podcasts

11 · Tools Setup

• 11 — Tools Setup
• 01 — Python Environment Setup
• 02 — GPU & Compute Strategy
• 03 — Development Environment
• 04 — Version Control for ML
• 05 — Hardware & Local Setup

12 · Portfolio

• 12 | Portfolio Architecture
• Rung 1: Math From Scratch
• Rung 2: Classical ML Battle
• Rung 3: Deep Learning From Scratch
• Rung 4: Transformer Lab
• Rung 5: LLM Engineering
• Rung 6: Production ML System
• Rung 7: Paper Reproduction
• Rung 8: Original Technical Writing
• Portfolio Presentation Guide
• Portfolio Timeline

13 · Discipline

• The Discipline Manifesto
• Sprint Cadence — The 2-Week Rhythm
• The Lab Notebook — Predict, Then Measure
• Benchmark Hygiene — How to Measure Honestly
• Daily Practice — The 30-Minute Habit
• Teach to Learn — The Feynman Method for ML
• 06 — Failure Modes of ML Mastery
• 07 — Motivation Sustainment
• 08 — Health & Burnout
• 09 — Interview & Assessment Conversion

99 · Pre Mortem

• 99 — Pre-Mortem
• Failure Mode 01 — Tutorial Hell & Paralysis
• Failure Mode 02 — Math Phobia / Black-Box Usage
• Failure Mode 03 — Scope Creep & Perfectionism
• Failure Mode 04 — Comparison & Impostor Syndrome
• Failure Mode 05 — Burnout & Overcommitment
• Failure Mode 06 — Job Pressure Derailment
• Failure Mode 07 — Hype Chasing
• Failure Mode 08 — Life & Family Risk
• 09 — Summary & Reset Protocol