AI Beginner Pathway Architect & Roadmap Navigator

1. Diagnostic Profile

🎯 Target Specialization Track

📐 Current Mathematics Background

💻 Current Python / Coding Level

⏱️ Available Time Budget

10 hrs/wk

⚡ Quick Start Archetype Presets

220 hrs

Total Curriculum

22 Wks

Estimated Duration

8 Nodes

Core Milestones

Feb 2027

Completion Target

Math/Foundations

Python/Data

Classical ML

Deep Learning

Specialization

Python Core & Vectorized Computing

Estimated Effort: 30 Hours

Phase 1: Code

Master Python 3 fundamentals, list comprehensions, OOP principles, NumPy vector operations, and Pandas dataframe manipulation.

Core Concepts:

Python control flow, NumPy broadcasting, Matrix dot products, Pandas aggregation, Matplotlib visualization.

⚠️ Common Pitfall: Falling into tutorial hell without writing independent scripts. Avoid copy-pasting tutorials without typing every line manually.

Recommended Free Resource:
Coursera Python for Everybody & fast.ai Computational Linear Algebra

🛠️ Milestone Project & Code Spec Required Prerequisite

Project: Vectorized Data Pipeline & EDA Tool

Build a local CLI tool that ingests CSV datasets, detects missing features, performs z-score normalization with NumPy, and plots class distributions.

import numpy as np import pandas as pd def normalize_features(X: np.ndarray) -> np.ndarray: """Vectorized z-score normalization""" mean = np.mean(X, axis=0) std = np.std(X, axis=0) + 1e-8 return (X - mean) / std

Wk	Topic / Milestone	Hrs

Curriculum hour totals and rounded schedule mismatch

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Read the explanation

The default NLP curriculum selects seven milestone records with estimated hours thirty, fifteen, twenty, thirty five, forty, thirty and thirty. These sum to two hundred. At ten weekly hours the summary ceiling is twenty weeks. The syllabus separately rounds each node up, giving three, two, two, four, four, three and three weeks, totaling twenty one. Bars compare summary twenty and sequential syllabus twenty one at nine pixels per week. The export therefore can describe a schedule extending beyond the headline duration. These are authored learning estimates rather than validated completion guarantees or evidence that a learner mastered the subject. No model assesses proficiency or adapts instruction here. Advanced mathematics changes its foundation hours to zero and statistics to five, while proficient coding changes Python hours to zero. The NLP total becomes one hundred forty, giving fourteen summary weeks at ten weekly hours. The syllabus still allocates at least one week to every node, even zero hour nodes. Its durations become one, one, one, four, four, three and three, totaling seventeen. Bars compare fourteen and seventeen at ten pixels per week. This is a rounding and minimum allocation consequence. The pathway uses the source node array order and filters graph edges for drawing; it does not solve an optimized prerequisite scheduler or verify learner competence. At twenty five weekly hours the default two hundred hours give eight summary weeks. Rounding each milestone separately yields two, one, one, two, two, two and two weeks, totaling twelve. Bars count eight and twelve at fifteen pixels per week. The target month is current time plus summary weeks times seven days, not the final syllabus week. Offline missing D3 throws during graph initialization after controls and downloads bind. Subsequent configuration edits update settings but graph rendering returns early before updating the visible metrics or empty syllabus. Actual JSON and Markdown downloads compute the selected curriculum independently and remain available. Native exports preserve this distinction without supplying the dependency, changing source controls, guaranteeing learning outcomes or claiming public publication.