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AI Machine Learning & Deep LearningLivebeginner8 hours

How Models Learn

Learn how AI systems improve from data, error signals, and repeated updates. This path teaches the core training loop, data quality, tuning decisions, and trustworthy evaluation without drowning the learner in heavy math.

Start Path

Modules

4

Rooms

14

Catalog Status

Live

Path Outline

Modules and Rooms

Modules that are already published show their live rooms below. Modules still being restored stay visible as roadmap placeholders so learners can see the full path direction.

1

What Learning Actually Is

Build the mental model for training before you touch datasets, metrics, or tuning loops.

5 live rooms
1
Learn

What Is a Model?

A model is a learned system that maps inputs to outputs. This room gives the learner their first accurate mental model without hype or hand-waving.

beginner35 min90 XP
Open room
2
Learn

Examples, Predictions, and Tasks

Machine learning starts with examples and outputs, not vague product promises. This room teaches what an example is and what prediction really means in ML.

beginner30 min95 XP
Open room
3
Learn

Features, Labels, and Targets

Features are what the model sees, and labels are what training compares the prediction against. This room teaches learners how to read a dataset the way a builder would.

beginner35 min95 XP
Open room
4
Learn

What Training Actually Changes

Training does not rewrite reality or magically create understanding. It changes the model's internal parameters so predictions improve over repeated examples.

beginner35 min100 XP
Open room
5
Assess

Module 1 Checkpoint: Read a Learning Problem

Pull together the first module by reading small ML scenarios the way a builder would. This checkpoint tests whether the learner can identify examples, outputs, features, labels, and what training changes.

beginner20 min85 XP
Open room
2

Data Is The Teacher

See how data quality, labeling, leakage, and split decisions shape model behavior before training even begins.

3 live rooms
1
Learn

Good Data, Bad Data

Learn why noisy labels, imbalance, duplicates, leakage, and biased sampling can ruin a training pipeline before the model even starts learning.

beginner20 min80 XP
Open room
2
Practice

Dataset Triage Lab

Inspect a toy churn dataset, identify missing labels, duplicate records, and split leakage, then prove what is wrong from the artifacts in the lab.

beginner30 min120 XP
Open room
3
Build

Repair the Training Set

Convert a weak dataset repair plan into a trustworthy one by blocking unlabeled rows, duplicates, and split leakage with explicit rules for this toy customer dataset.

intermediate35 min140 XP
Open room
3

The Training Loop

Understand prediction, loss, parameter updates, and tuning choices with a toy learner you can reason about directly.

3 live rooms
1
Learn

Prediction, Loss, and Updates

Learn the core training loop by following predictions, measuring loss, and explaining how updates reduce error over repeated examples.

beginner25 min100 XP
Open room
2
Learn

Gradient Descent Intuition

Build intuition for gradient descent, learning rate, and unstable updates without turning the lesson into a math lecture.

beginner25 min100 XP
Open room
3
Build

Tune the Toy Learner

Improve a toy training recipe by stabilizing updates, giving the learner enough passes to converge, and adding safeguards against overfitting in this controlled lab setup.

intermediate35 min150 XP
Open room
4

Generalization And Trustworthy Evaluation

Finish by learning why good training scores can still hide weak models and how to evaluate a baseline responsibly.

3 live rooms
1
Learn

Overfitting, Underfitting, and Generalization

Learn why strong training performance can still hide a weak model, and how train, validation, and test behavior reveal what is really happening.

beginner25 min100 XP
Open room
2
Practice

Evaluation Debug Lab

Investigate a misleading model report, identify the bad metric and leaked split, and explain why the experiment cannot be trusted yet.

intermediate30 min130 XP
Open room
3
Build

Baseline Model Capstone

Assemble a trustworthy baseline experiment plan with the right split, metric family, baseline comparison, and explicit imbalance handling before any model is declared ready.

intermediate40 min180 XP
Open room