Build practical programming and technology skills

Machine Learning Fundamentals

Prepare data, train baseline regression and classification models, evaluate predictions, detect leakage and overfitting, and document limitations.

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PractitionerDifficulty

40 hoursEstimated time

8 modules32 lessons

5 guided labsKnowledge check

Plan your learning

Path Brief

Skills you will practice

Data Ai · Machine Learning Fundamentals
Data Ai · Machine Learning Problem Framing
Data Ai · Data Preparation And Splitting
Data Ai · Regression Models
Data Ai · Classification Models
Data Ai · Feature Engineering And Pipelines

Prerequisites

Python Fundamentals
Data Analysis Python
Statistics Data Science

Tools

A current web browser
A code editor or terminal appropriate to the path
A safe local or virtual lab environment

Before you begin

What you need

Python Fundamentals
Data Analysis Python
Statistics Data Science

Step-by-step curriculum

Learning modules

Complete lessons in order or open the module that matches your current goal. Your lesson progress is saved on this device.

01Machine Learning Problem FramingBuild practical skill in machine learning problem framing and connect it to the outcomes of Machine Learning Fundamentals.4 lessons · 1 lab

Lesson 1

Machine Learning Problem Framing: Core Concepts for Machine Learning Fundamentals

Understand the concepts, decisions, and vocabulary you need before applying Machine Learning Problem Framing in Machine Learning Fundamentals.

25 min

Lesson 2

Set Up and Explore Machine Learning Problem Framing in Machine Learning Fundamentals

Prepare the tools, data, project state, or test environment needed to explore Machine Learning Problem Framing safely and repeatably.

25 min

Lesson 3

Build a Practical Machine Learning Problem Framing Example in Machine Learning Fundamentals

Define a prediction problem, create a leakage-safe split, build a baseline pipeline, and document the evaluation plan.

30 min

Lesson 4

Debug Common Machine Learning Problem Framing Problems in Machine Learning Fundamentals

Recognize common failure modes in Machine Learning Problem Framing, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Safe

Guided Lab: Machine Learning Problem Framing in Machine Learning Fundamentals

Machine Learning Fundamentals — Machine Learning Problem Framing lab: Define a prediction problem, create a leakage-safe split, build a baseline pipeline, and document the evaluation plan.

Not started

02Data Preparation and SplittingBuild practical skill in data preparation and splitting and connect it to the outcomes of Machine Learning Fundamentals.4 lessons · 1 lab

Lesson 1

Data Preparation and Splitting: Core Concepts for Machine Learning Fundamentals

Understand the concepts, decisions, and vocabulary you need before applying Data Preparation and Splitting in Machine Learning Fundamentals.

25 min

Lesson 2

Set Up and Explore Data Preparation and Splitting in Machine Learning Fundamentals

Prepare the tools, data, project state, or test environment needed to explore Data Preparation and Splitting safely and repeatably.

25 min

Lesson 3

Build a Practical Data Preparation and Splitting Example in Machine Learning Fundamentals

Define a prediction problem, create a leakage-safe split, build a baseline pipeline, and document the evaluation plan.

30 min

Lesson 4

Debug Common Data Preparation and Splitting Problems in Machine Learning Fundamentals

Recognize common failure modes in Data Preparation and Splitting, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Data Preparation and Splitting in Machine Learning Fundamentals

Machine Learning Fundamentals — Data Preparation and Splitting lab: Define a prediction problem, create a leakage-safe split, build a baseline pipeline, and document the evaluation plan.

Not started

03Regression ModelsBuild practical skill in regression models and connect it to the outcomes of Machine Learning Fundamentals.4 lessons · 1 lab

Lesson 1

Regression Models: Core Concepts for Machine Learning Fundamentals

Understand the concepts, decisions, and vocabulary you need before applying Regression Models in Machine Learning Fundamentals.

25 min

Lesson 2

Set Up and Explore Regression Models in Machine Learning Fundamentals

Prepare the tools, data, project state, or test environment needed to explore Regression Models safely and repeatably.

25 min

Lesson 3

Build a Practical Regression Models Example in Machine Learning Fundamentals

Fit a simple regression model, compare it with a baseline, report MAE/RMSE, and inspect large residuals.

30 min

Lesson 4

Debug Common Regression Models Problems in Machine Learning Fundamentals

Recognize common failure modes in Regression Models, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Regression Models in Machine Learning Fundamentals

Machine Learning Fundamentals — Regression Models lab: Fit a simple regression model, compare it with a baseline, report MAE/RMSE, and inspect large residuals.

Not started

04Classification ModelsBuild practical skill in classification models and connect it to the outcomes of Machine Learning Fundamentals.4 lessons · 1 lab

Lesson 1

Classification Models: Core Concepts for Machine Learning Fundamentals

Understand the concepts, decisions, and vocabulary you need before applying Classification Models in Machine Learning Fundamentals.

25 min

Lesson 2

Set Up and Explore Classification Models in Machine Learning Fundamentals

Prepare the tools, data, project state, or test environment needed to explore Classification Models safely and repeatably.

25 min

Lesson 3

Build a Practical Classification Models Example in Machine Learning Fundamentals

Train a classifier and compare precision/recall at two thresholds.

30 min

Lesson 4

Debug Common Classification Models Problems in Machine Learning Fundamentals

Recognize common failure modes in Classification Models, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Classification Models in Machine Learning Fundamentals

Machine Learning Fundamentals — Classification Models lab: Train a classifier and compare precision/recall at two thresholds.

Not started

05Feature Engineering and PipelinesBuild practical skill in feature engineering and pipelines and connect it to the outcomes of Machine Learning Fundamentals.4 lessons · 1 lab

Lesson 1

Feature Engineering and Pipelines: Core Concepts for Machine Learning Fundamentals

Understand the concepts, decisions, and vocabulary you need before applying Feature Engineering and Pipelines in Machine Learning Fundamentals.

25 min

Lesson 2

Set Up and Explore Feature Engineering and Pipelines in Machine Learning Fundamentals

Prepare the tools, data, project state, or test environment needed to explore Feature Engineering and Pipelines safely and repeatably.

25 min

Lesson 3

Build a Practical Feature Engineering and Pipelines Example in Machine Learning Fundamentals

Define a prediction problem, create a leakage-safe split, build a baseline pipeline, and document the evaluation plan.

30 min

Lesson 4

Debug Common Feature Engineering and Pipelines Problems in Machine Learning Fundamentals

Recognize common failure modes in Feature Engineering and Pipelines, use the relevant diagnostics, and verify the correction.

25 min

Guided lab · 55 min · Moderate

Guided Lab: Feature Engineering and Pipelines in Machine Learning Fundamentals

Machine Learning Fundamentals — Feature Engineering and Pipelines lab: Define a prediction problem, create a leakage-safe split, build a baseline pipeline, and document the evaluation plan.

Not started

06Model EvaluationBuild practical skill in model evaluation and connect it to the outcomes of Machine Learning Fundamentals.4 lessons

Lesson 1

Model Evaluation: Core Concepts for Machine Learning Fundamentals

Understand the concepts, decisions, and vocabulary you need before applying Model Evaluation in Machine Learning Fundamentals.

25 min

Lesson 2

Set Up and Explore Model Evaluation in Machine Learning Fundamentals

Prepare the tools, data, project state, or test environment needed to explore Model Evaluation safely and repeatably.

25 min

Lesson 3

Build a Practical Model Evaluation Example in Machine Learning Fundamentals

Evaluate a classifier with precision, recall, F1, a confusion matrix, and cross-validation, then explain which error matters most for the scenario.

30 min

Lesson 4

Debug Common Model Evaluation Problems in Machine Learning Fundamentals

Recognize common failure modes in Model Evaluation, use the relevant diagnostics, and verify the correction.

25 min

07Overfitting, Validation, and TuningBuild practical skill in overfitting, validation, and tuning and connect it to the outcomes of Machine Learning Fundamentals.4 lessons

Lesson 1

Overfitting, Validation, and Tuning: Core Concepts for Machine Learning Fundamentals

Understand the concepts, decisions, and vocabulary you need before applying Overfitting, Validation, and Tuning in Machine Learning Fundamentals.

25 min

Lesson 2

Set Up and Explore Overfitting, Validation, and Tuning in Machine Learning Fundamentals

Prepare the tools, data, project state, or test environment needed to explore Overfitting, Validation, and Tuning safely and repeatably.

25 min

Lesson 3

Build a Practical Overfitting, Validation, and Tuning Example in Machine Learning Fundamentals

Build a form or request validator with valid, missing, malformed, and boundary inputs.

30 min

Lesson 4

Debug Common Overfitting, Validation, and Tuning Problems in Machine Learning Fundamentals

Recognize common failure modes in Overfitting, Validation, and Tuning, use the relevant diagnostics, and verify the correction.

25 min

08Reproducibility and Responsible UseBuild practical skill in reproducibility and responsible use and connect it to the outcomes of Machine Learning Fundamentals.4 lessons

Lesson 1

Reproducibility and Responsible Use: Core Concepts for Machine Learning Fundamentals

Understand the concepts, decisions, and vocabulary you need before applying Reproducibility and Responsible Use in Machine Learning Fundamentals.

25 min

Lesson 2

Set Up and Explore Reproducibility and Responsible Use in Machine Learning Fundamentals

Prepare the tools, data, project state, or test environment needed to explore Reproducibility and Responsible Use safely and repeatably.

25 min

Lesson 3

Build a Practical Reproducibility and Responsible Use Example in Machine Learning Fundamentals

Define a prediction problem, create a leakage-safe split, build a baseline pipeline, and document the evaluation plan.

30 min

Lesson 4

Debug Common Reproducibility and Responsible Use Problems in Machine Learning Fundamentals

Recognize common failure modes in Reproducibility and Responsible Use, use the relevant diagnostics, and verify the correction.

25 min

Portfolio integration

Capstone Project

Build and Evaluate a Predictive Model with a Model Card

Combine the path skills in a portfolio-ready project with clear functional requirements, tests, verification evidence, and operating notes.

GuidedIndependentPortfolio

Acceptance criteria

The core workflow works from start to finish.
Invalid input or failure states are handled clearly.
The result is tested and documented.
A reviewer can reproduce the setup and verification.

Continue in Programming

Related Programming

Skill assessment

Machine Learning Fundamentals

Path Brief

Skills you will practice

Prerequisites

Tools

What you need

Learning modules

Machine Learning Problem Framing: Core Concepts for Machine Learning Fundamentals

Set Up and Explore Machine Learning Problem Framing in Machine Learning Fundamentals

Build a Practical Machine Learning Problem Framing Example in Machine Learning Fundamentals

Debug Common Machine Learning Problem Framing Problems in Machine Learning Fundamentals

Guided Lab: Machine Learning Problem Framing in Machine Learning Fundamentals

Data Preparation and Splitting: Core Concepts for Machine Learning Fundamentals

Set Up and Explore Data Preparation and Splitting in Machine Learning Fundamentals

Build a Practical Data Preparation and Splitting Example in Machine Learning Fundamentals

Debug Common Data Preparation and Splitting Problems in Machine Learning Fundamentals

Guided Lab: Data Preparation and Splitting in Machine Learning Fundamentals

Regression Models: Core Concepts for Machine Learning Fundamentals

Set Up and Explore Regression Models in Machine Learning Fundamentals

Build a Practical Regression Models Example in Machine Learning Fundamentals

Debug Common Regression Models Problems in Machine Learning Fundamentals

Guided Lab: Regression Models in Machine Learning Fundamentals

Classification Models: Core Concepts for Machine Learning Fundamentals

Set Up and Explore Classification Models in Machine Learning Fundamentals

Build a Practical Classification Models Example in Machine Learning Fundamentals

Debug Common Classification Models Problems in Machine Learning Fundamentals

Guided Lab: Classification Models in Machine Learning Fundamentals

Feature Engineering and Pipelines: Core Concepts for Machine Learning Fundamentals

Set Up and Explore Feature Engineering and Pipelines in Machine Learning Fundamentals

Build a Practical Feature Engineering and Pipelines Example in Machine Learning Fundamentals

Debug Common Feature Engineering and Pipelines Problems in Machine Learning Fundamentals

Guided Lab: Feature Engineering and Pipelines in Machine Learning Fundamentals

Model Evaluation: Core Concepts for Machine Learning Fundamentals

Set Up and Explore Model Evaluation in Machine Learning Fundamentals

Build a Practical Model Evaluation Example in Machine Learning Fundamentals

Debug Common Model Evaluation Problems in Machine Learning Fundamentals

Overfitting, Validation, and Tuning: Core Concepts for Machine Learning Fundamentals

Set Up and Explore Overfitting, Validation, and Tuning in Machine Learning Fundamentals

Build a Practical Overfitting, Validation, and Tuning Example in Machine Learning Fundamentals

Debug Common Overfitting, Validation, and Tuning Problems in Machine Learning Fundamentals

Reproducibility and Responsible Use: Core Concepts for Machine Learning Fundamentals

Set Up and Explore Reproducibility and Responsible Use in Machine Learning Fundamentals

Build a Practical Reproducibility and Responsible Use Example in Machine Learning Fundamentals

Debug Common Reproducibility and Responsible Use Problems in Machine Learning Fundamentals

Capstone Project

Build and Evaluate a Predictive Model with a Model Card

Acceptance criteria

Test your understanding

Related Programming

Check your starting point

Plan the capstone

Explore related paths