Carry the roadmap in your pocket.
A responsive web version of the learning system: roadmap, modules, projects, labs, checklists, prompts, rubrics, and sample data.
Start Fast
Start Here
Start Here Use this folder as a learning operating system, not just a reading list. First Day Setup 1. Read ROADMAP.md to understand the full path. 2. Open PROGRESS.md and mark you
Progress Dashboard
Progress Dashboard Update this once per week. Current Focus Current month: Current module: Current project: This week's output: Main blocker: Next action: Module Progress Module St
Monthly Plan
Monthly Plan This file turns the 12 month roadmap into weekly execution. Month 1: ML Fundamentals Week 1 Review linear algebra basics. Review probability and statistics basics. Cre
Today
Today Date: Focus Module: Project: Time available: Today's Task Task: Definition of Done Artifact created or updated. Notes written. Next action defined. Blocker Blocker: Fix attem
Modules
Module 01: Machine Learning Fundamentals
Module 01: Machine Learning Fundamentals Goal Understand how classical machine learning works, why models fail, how to evaluate them, and how to turn a model into a usable system.
Module 02: Deep Learning
Module 02: Deep Learning Goal Understand neural networks well enough to train, debug, regularize, evaluate, export, and serve a model. Core Topics Perceptron and multilayer neural
Module 03: Transformers and LLMs
Module 03: Transformers and LLMs Goal Understand transformer architecture, LLM behavior, prompt design, structured outputs, and cost aware inference. Core Topics Tokenization. Embe
Module 04: RAG Engineering
Module 04: RAG Engineering Goal Build retrieval augmented generation systems that produce grounded answers with citations, access control, evaluation, and operational visibility. C
Module 05: AI Agents and Tool Calling
Module 05: AI Agents and Tool Calling Goal Build agents that use tools safely, validate actions, preserve state, support human confirmation, and create audit logs. Core Topics Tool
Module 06: AI Evaluation, LLMOps, and MLOps
Module 06: AI Evaluation, LLMOps, and MLOps Goal Create repeatable evaluation and monitoring systems so AI changes can be accepted or rejected with evidence. Core Topics Golden dat
Module 07: Docker and Kubernetes
Module 07: Docker and Kubernetes Goal Package AI systems consistently and deploy them to a local Kubernetes environment with health checks, scaling, configuration, and secrets. Doc
Module 08: Cloud Architecture
Module 08: Cloud Architecture Goal Design reliable, secure, scalable, observable, and cost aware cloud architectures for AI systems. Core Areas Compute: VMs, containers, serverless
Module 09: Security and Infrastructure as Code
Module 09: Security and Infrastructure as Code Goal Treat security and repeatable infrastructure as core engineering skills, not final cleanup tasks. Security Topics IAM and RBAC.
Module 10: AI Product and Transformation
Module 10: AI Product and Transformation Goal Turn business problems into AI opportunities only when AI is appropriate, then define feasibility, KPIs, ROI, risks, and an implementa
Module 11: Enterprise AI Project
Module 11: Enterprise AI Project Goal Combine RAG, agents, evaluation, cloud architecture, security, observability, and product thinking into one production style system. Required
Module 12: Portfolio, Certification, and Final Review
Module 12: Portfolio, Certification, and Final Review Goal Package the work into a clear proof of skill portfolio and complete one meaningful certification path if it supports the