Complete for freeIntermediateBrowser simulator included

Course 16

Beginner Machine Learning with Python

Build reproducible scikit-learn pipelines, compare KNN, linear and logistic regression, and decision trees, and evaluate predictions with suitable metrics. 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 ML concept simulations with learner-created Python and analysis files made in freely installable or open tools. The C4T page does not run notebooks or train models.

Before you begin

Requirements & setup for Beginner Machine Learning with Python

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

The always-free pathway combines ML concept simulations with learner-created Python and analysis files made in freely installable or open tools. The C4T page does not run notebooks or train models.

$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 CPU ML Concept Simulator
Open the concept simulator and read its scenario, assumptions, and limitations.
About 5 minOpen C4T CPU ML Concept Simulator (opens in a new tab)
3. C4T CPU ML Concept Simulator: step 2
Make a prediction, adjust the available controls, and compare the result with the explanation.
About 5 min
4. C4T CPU ML Concept Simulator: step 3
Record what changed and why; repeat with at least one different input.
About 5 min
5. C4T CPU ML 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 CPU ML 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 CPU ML Concept Simulator

Course need

C4T CPU ML 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 CPU ML Concept Simulator

No-hardware path

Classical ML 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

Construct an end-to-end scikit-learn workflow with protected test data.
Train and interpret KNN, regression, and decision-tree models.
Calculate and choose classification and regression metrics.
Diagnose overfitting and recommend a model from comparative evidence.

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

Preprocessing and baseline notebook

Build a leak-resistant split and preprocessing workflow, then compare a baseline with one trained model.

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

Metric and error analysis

Calculate confusion metrics, inspect errors, diagnose fit, and recommend a threshold or model change.

Final assessment

Classical model comparison

Compare at least three models on one documented dataset using protected train-test separation, preprocessing, multiple metrics, error analysis, an overfitting discussion, and a justified recommendation. Deliverables: Documented dataset and preprocessing, Protected train-test workflow, At least three trained models, Multiple appropriate metrics and error analysis, and Fit diagnosis and final recommendation.

Prerequisites & pacing

Skills assumed

Python projects
DataFrame basics
Foundational AI vocabulary

Recommended prior courses

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

Choose your pace

self paced: 10 weeks at 5 hours per week
weekly: 10 weeks at 5 hours per week

Part of this learning path

🧠 Data and Artificial Intelligence · step 4 of 7
Learn to read data honestly, then train, evaluate, and interrogate machine-learning models.

Course created by Zafir Shamsi.