Inicio Explorar cursos AI & Machine Learning Practical Machine Learning with scikit-learn
Curso gratuito English Certificado verificado 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 valoraciones)
95 estudiantes inscritos
323 visualizaciones
30 lessons
1 con narración de audio
~6h en total
A tu ritmo, acceso de por vida

¿De qué trata este curso?

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.

¿Cuáles son los datos clave de este curso?

Precio Gratis
Certificado Certificado verificado
Duración 6h
Lecciones 30
Idioma English
Nivel Beginner
Examen A tu ritmo, acceso de por vida

Programa

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

Certificado de finalización

Al terminar este curso desbloquearás un certificado verificado — descargable en PDF, con número único y enlace de verificación pública.

Cada certificado tiene un enlace verificable único y un código QR.

Preguntas frecuentes

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?

Tras finalizar este curso podrás descargar un certificado verificado con un número único y enlace público de verificación.

Opiniones de los estudiantes

4.3
3 valoraciones
5
1
4
2
3
0
2
0
1
0
D
David K. Verificado · 17 may. 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. Verificado · 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. Verificado · 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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