Learning Path (Async, Self-Paced)

Use this page as the default sequence for the curriculum. Each module has a recommended order, estimated pace, and a checkpoint from the Checkpoints page.

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Module 1: Orientation + AI/ML Framing

• Estimated time: 2-3 hours
• Lessons:
  • Intro
  • What is AI/ML?
  • Local Setup (optional)
• Goal: Understand scope, terminology, and tooling options.
• Checkpoint: Module 1 on the checkpoints page.

Module 2: Python/NumPy Data Workflow Basics

• Estimated time: 4-6 hours
• Lessons:
  • Numpy
• Goal: Build and manipulate arrays, masks, and vectorized operations for ML data workflows.
• Checkpoint: Module 2.

Module 3: ML Fundamentals + Evaluation

• Estimated time: 5-7 hours
• Lessons:
  • Intro to Machine Learning
  • Feature Engineering Basics
  • Evaluation Basics
• Goal: Learn data split/train/evaluate/iterate loop with leakage awareness.
• Checkpoint: Module 3.

Module 4: Linear Models + Optimization Intuition

• Estimated time: 5-7 hours
• Lessons:
  • Optimization & Gradient Descent
• Mini (core): Optimization Playground
• Goal: Understand how parameters update and how learning rate affects convergence.
• Checkpoint: Module 4.

Module 5: Unsupervised Learning

• Estimated time: 6-8 hours
• Lessons:
  • Intro to Machine Learning - clustering and dimensionality reduction sections
• Mini (core): Spotify Music Recommendation
• Goal: Build intuition for clustering workflows and interpretation.
• Checkpoint: Module 5.

Module 6: Intro Neural Networks (Extension)

• Estimated time: 6-8 hours
• Lessons:
  • Artificial Neural Networks
• Mini (optional core-extension): Artificial Neural Network Mini
• Goal: Understand perceptrons, layers, and basic training intuition.
• Checkpoint: Module 6.

Module 7: NLP Foundations

• Estimated time: 6-8 hours
• Lessons:
  • Natural Language Processing
• Mini: NLP with Disaster Tweets
• Goal: Build practical text preprocessing and representation workflow.
• Checkpoint: Module 7.

Module 8: RL Foundations

• Estimated time: 6-8 hours
• Lessons:
  • Reinforcement Learning
• Mini (local recommended): Snake with RL
• Goal: Understand state/action/reward and exploration-exploitation tradeoffs.
• Checkpoint: Module 8.

Module 9: Consolidation via Mini Selection

• Estimated time: 8-12 hours
• Goal: Complete at least two polished minis from different domains (for example Optimization + NLP).
• Output: Reproducible notebooks and short markdown summaries.
• Checkpoint: Module 9.

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Core Mini Set (Mastery Signal)

To complete the async pathway, finish this core set:

• Optimization Playground
• Spotify Music Recommendation
• One of: NLP Mini, ANN Mini, or RL Mini

Each mini should include a cleaned notebook, output plots/tables, and a short write-up of decisions and results.

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Table of contents

• Checkpoints