Complete for freeAdvanced intermediateBrowser simulator included

Course 19

Intermediate Machine Learning and Model Evaluation

Build rigorous ML comparisons with feature engineering, pipelines, cross-validation, tuning, imbalance handling, ensembles, leakage checks, calibration, interpretation, and sliced error analysis. The course combines structured lessons, guided browser concept simulations, and learner-created project files made with freely installable or open tools described in the course instructions. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure.

Free completion promise: The always-free pathway combines evaluation concept simulations with learner-created pipeline and report files made in freely installable or open tools. The C4T page does not run searches, notebooks, or models.

Before you begin

Requirements & setup for Intermediate Machine Learning and Model Evaluation

Start with the complete free pathway below. Hardware, subscriptions, paid APIs, and cloud credits are never required.

Start here · free path

Your complete $0 setup

$0 required

Setup checklist

0 of 6 complete

This checklist is a temporary setup aid and resets when the page reloads. Saved course progress begins in the setup lessons below.

1. Choose the free learning path
Use the browser-based tools and simulations throughout this course. Paid software, APIs, cloud credits, GPUs, and physical equipment are optional.
About 2 min
2. Set up C4T Model Evaluation Concept Simulator
Open the concept simulator and read its scenario, assumptions, and limitations.
About 5 minOpen C4T Model Evaluation Concept Simulator (opens in a new tab)
3. C4T Model Evaluation Concept Simulator: step 2
Make a prediction, adjust the available controls, and compare the result with the explanation.
About 5 min
4. C4T Model Evaluation Concept Simulator: step 3
Record what changed and why; repeat with at least one different input.
About 5 min
5. C4T Model Evaluation Concept Simulator: step 4
For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.
About 5 min
6. Run the setup check
C4T Model Evaluation Concept Simulator: Complete one prediction-and-explanation cycle and record the evidence you would carry into a learner-created project file.
About 5 min

Software

1 item

C4T Model Evaluation Concept Simulator

Course need

C4T Model Evaluation Concept Simulator

Free

A free browser concept simulator with adjustable values, a prediction prompt, deterministic feedback, and a short explanatory trace. This guided page does not execute code, train models, run databases, create repositories, access hardware, or provide hosted infrastructure. Use keyboard controls and the text trace or written alternative when a visual or pointer interaction is not suitable.

Open the concept simulator and read its scenario, assumptions, and limitations.
Make a prediction, adjust the available controls, and compare the result with the explanation.
Record what changed and why; repeat with at least one different input.
For applied work, follow the course instructions to create and keep your own project files with a freely installable or open tool appropriate to the subject.

Open C4T Model Evaluation Concept Simulator

No-hardware path

Model Evaluation Concept Simulator

Explore course concepts through adjustable values, prediction, deterministic feedback, and an explanatory trace. No hardware is required.

Hardware

Nothing required

No physical hardware is needed for this course.

Accounts

Nothing required

No external account is needed for this course.

What you will be able to do

Learning outcomes

Engineer features and preprocessing inside reproducible pipelines.
Use cross-validation and constrained hyperparameter search correctly.
Address imbalance and compare ensembles without data leakage.
Evaluate calibration, interpretation, and error slices before recommending a model.

Included downloads

Free starter assets

Original · synthetic · licensed

These small files contain no personal data, credentials, or secrets. Dataset fixtures are CC0 and starter code is MIT licensed.

Starter pack guide & licensesSee what each original synthetic dataset, fixture, and starter file contains.ML evaluation fixtureFixed, synthetic classification, retrieval, research, and deployment values.

Build real work

Assignments & capstone

Free tools accepted

Pipeline and validation audit

Create a reproducible pipeline, choose cross-validation, and identify potential leakage before tuning.

The always-free pathway combines evaluation concept simulations with learner-created pipeline and report files made in freely installable or open tools. The C4T page does not run searches, notebooks, or models. Submit a project file, text trace, screenshots with descriptions, or equivalent simulator evidence.

Imbalance, calibration, and slice report

Compare imbalance strategies and model families, then evaluate calibration and error slices.

Final assessment

Rigorous model-comparison report

Deliver a reproducible comparison with cross-validation, bounded tuning, imbalance handling, leakage checks, calibration, interpretation, error analysis, recommendation, and limitations. Deliverables: Reproducible feature and pipeline code, Cross-validation and search evidence, Imbalance and leakage analysis, Calibration, interpretation, and error slices, and Final recommendation with limitations.

Prerequisites & pacing

Skills assumed

A complete scikit-learn workflow and familiarity with classification metrics

Recommended prior courses

Beginner Machine Learning with Python

These are advisory, not locks. If you already have the skills listed above, enroll directly.

Choose your pace

self paced: 12 weeks at 5 hours per week
weekly: 12 weeks at 5 hours per week

Part of this learning path

🧠 Data and Artificial Intelligence · step 5 of 7
Learn to read data honestly, then train, evaluate, and interrogate machine-learning models.

Course created by Zafir Shamsi.