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ML & GenAI
Learning Lab

Interactive, browser-based playbooks for data scientists, ML engineers, and GenAI practitioners. Use decision trees and visual guides to choose algorithms, metrics, GenAI techniques, and certifications with confidence.

How to Use This Lab

Each module is a small, self-contained playbook. You can drop in for 10–30 minutes, answer a few focused questions, and leave with a concrete decision or checklist.

  1. 01

    Pick the question you’re stuck on.

    Algorithm, metric, GenAI technique, evaluation, or exam prep.

  2. 02

    Walk through a guided decision flow.

    Answer simple prompts and follow the branches that match your use case.

  3. 03

    Capture the outcome and rationale.

    Use the final recommendation as a design note, study aid, or team decision record.

Available Resources

07 modules · Beginner to Advanced

Start with the Beginner module if you're new, then progress through Intermediate and Advanced guides.

Beginner
Start here
00

GenAI Foundations Navigator

Beginner-friendly tour of GenAI basics: grounding, RAG components, prompt engineering, core parameters, and when to consider fine-tuning or model compression.

GenAI Basics ~10–15 min
Intermediate
04 modules
01

Algorithm Selector

Interactive decision tree to find the optimal ML algorithm for your specific problem type and data characteristics.

Decision Tree ~15 min
02

Feature Engineering Playbook

Interactive decision tree for encoding, scaling, outlier detection, feature creation, and selection across tabular, text, time-series, and image data.

Feature Engineering ~20 min
03

Metric Decision Tree

Navigate the landscape of model evaluation metrics. Choose the right metric for classification, regression, and beyond.

Evaluation ~20 min
04

AWS AI Practitioner

Comprehensive study guide with interactive flashcards and quizzes for the AWS Certified AI Practitioner exam.

Certification ~30–45 min
Advanced
02 modules
05

GenAI Technique Selector

Interactive decision tree to choose the right LLM optimization: RAG, Fine-tuning, LoRA, Quantization, and more.

LLM Optimization ~25+ min
06

FM Evaluation Metrics

Choose the right metrics for LLM evaluation: Perplexity, ROUGE, BERTScore, RAGAS, LLM-as-Judge, and more.

LLM Metrics ~20 min

Suggested Paths

Not sure where to start? These lightweight paths pair the modules into simple journeys.

Path_A · Getting Started

New to ML & GenAI

  • 1. GenAI Foundations Navigator
  • 2. Algorithm Selector
  • 3. Feature Engineering Playbook
  • 4. Metric Decision Tree

Path_B · GenAI

GenAI Engineer Track

  • 1. GenAI Foundations Navigator
  • 2. GenAI Technique Selector
  • 3. FM Evaluation Metrics

Path_C · Certification

AWS AI Practitioner Prep

  • 1. AWS AI Practitioner Guide
  • 2. Metric Decision Tree
  • 3. (Optional) GenAI Technique Selector

About This Lab

A curated collection of interactive learning tools designed for data scientists and ML engineers. Each resource is built to provide hands-on, practical guidance for real-world machine learning decisions.

Created by Tarek Atwan

07
Interactive Modules
100%
Browser Based

What’s new

· Feature Engineering Playbook v1.0 · GenAI Foundations Navigator (Beginner) · GenAI Technique Selector v1.0 · FM Evaluation Metrics v1.0

Upcoming ideas: MLOps readiness checklist, experiment design aide.