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Machine Learning & AI — Project Fortress

Machine Learning & AI — Project Fortress

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

Index

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