Startseite Kurse durchsuchen AI & Machine Learning Practical Machine Learning with scikit-learn
Kostenloser Kurs English Verifiziertes Zertifikat Beginner #ai #machine learning #chatgpt #automation #machine #learning #scikit-learn #will

Practical Machine Learning with scikit-learn

Learn practical machine learning with scikit-learn from the ground up. You will turn business problems into models, prepare data, evaluate results, and build reliable workflows. By the end, you can create strong baselines and use scikit-learn with confidence in your daily work.

4.3 (3 Bewertungen)
95 eingeschriebene Lernende
323 Aufrufe
30 lessons
1 mit Audio-Erzählung
~6h gesamt
Im eigenen Tempo, lebenslanger Zugriff

Worum geht es in diesem Kurs?

This beginner-friendly course introduces practical machine learning for working professionals using scikit-learn. You will learn how to frame real business problems, prepare data, train and evaluate models, improve results, and build reliable end-to-end workflows. Each module starts with a short orientation, each lesson uses real-world examples, and every lesson ends with quick practical exercises you can complete in 5-15 minutes. By the end of the course, you will be able to create solid baseline models, interpret results, avoid common mistakes, and confidently use scikit-learn in day-to-day analytical work.

Was sind die wichtigsten Fakten zu diesem Kurs?

Preis Kostenlos
Zertifikat Verifiziertes Zertifikat
Dauer 6h
Lektionen 30
Sprache English
Niveau Beginner
Prüfung Im eigenen Tempo, lebenslanger Zugriff

Lehrplan

6 modules · 30 lessons

1
Module 1. Getting Started with Machine Learning and scikit-learn
5 lessons
  • 1. What machine learning is and when to use it
  • 2. Understanding the scikit-learn ecosystem
  • 3. The machine learning workflow from start to finish
  • 4. Working safely in a notebook environment
  • 5. Introducing data, features, and targets through a business example
2
Module 2. Preparing Data for Machine Learning
5 lessons
  • 1. Loading and exploring datasets with pandas
  • 2. Handling missing values and basic cleaning
  • 3. Encoding categorical variables
  • 4. Scaling and transforming numeric features
  • 5. Building preprocessing pipelines the right way
3
Module 3. Regression: Predicting Continuous Values
5 lessons
  • 1. What regression problems look like
  • 2. Training your first linear regression model
  • 3. Evaluating regression performance
  • 4. Improving regression with feature engineering
  • 5. Comparing regression models and choosing a baseline
4
Module 4. Classification: Predicting Categories
5 lessons
  • 1. Understanding classification tasks
  • 2. Training a logistic regression classifier
  • 3. Measuring classification quality
  • 4. Handling imbalanced classes and threshold tuning
  • 5. Comparing classification models and making a recommendation
5
Module 5. Model Evaluation, Validation, and Tuning
5 lessons
  • 1. Why train/test splits matter
  • 2. Cross-validation for more reliable estimates
  • 3. Hyperparameter tuning without overcomplicating things
  • 4. Avoiding data leakage and other evaluation traps
  • 5. Building a practical evaluation report
6
Module 6. Pipelines, End-to-End Workflows, and Next Steps
5 lessons
  • 1. Combining preprocessing and modeling in one pipeline
  • 2. Using column-specific preprocessing with ColumnTransformer
  • 3. Saving models and making them reusable
  • 4. Presenting results to stakeholders
  • 5. Your next steps in practical machine learning

Abschlusszertifikat

Nach Abschluss des Kurses schaltest du ein verifiziertes Zertifikat frei — als PDF herunterladbar, mit eindeutiger Nummer und Verifizierungslink.

Jedes Zertifikat hat einen eindeutigen Verifizierungslink und QR-Code.

Häufig gestellte Fragen

Is this course free to start?

Yes. You can start learning this course for free.

Who is this course for?

This course is designed for learners who want practical, job-relevant skills and a structured path from basics to application.

Do I need prior experience?

No prior experience is required unless specific prerequisites are listed in the course requirements section.

How long does this course take?

The estimated total study time for this course is 6h.

How does the certificate work?

Nach Abschluss des Kurses kannst du ein verifiziertes Zertifikat mit eindeutiger Nummer und öffentlichem Verifizierungslink herunterladen.

Bewertungen von Lernenden

4.3
3 Bewertungen
5
1
4
2
3
0
2
0
1
0
D
David K. Verifiziert · 17 Mai 2026

Clear and practical

Liked the real business problem framing and the quick exercises at the end of each lesson. I just wish a couple of the model evaluation sections went a bit deeper.

M
Matthew D. Verifiziert · 13 Feb 2026

I finally got a solid handle on using scikit-learn to prep data, train a model, and check results without getting lost. The end-to-end workflow part was especially useful.

O
Olivia H. Verifiziert · 22 Jul 2025

Useful beginner path

The short orientations before each module made it easy to stay on track, and the real-world examples kept it grounded. I would’ve liked a few more practice tasks, but the lesson endings were still handy.

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