Master Hyperparameter Tuning with Grid and Random SearchPublished 9/2026
Created by Soledad Galli, Train in Data Team
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate |
Genre: eLearning |
Language: English + subtitle |
Duration: 21 Lectures ( 2h 9m ) |
Size: 985.6 MB
⚡ Why tune hyperparamaters
⚡ Cross-validation frameworks
⚡ Grid search
⚡ Random search
Requirements❗ Basic knowledge of machine learning (i.e., linear and logistic regression and random forests models)
❗ Familiarity with gradient boosting machines, i.e., xgboost, lightGBMs
❗ Understanding of model evaluation metrics
DescriptionWelcome to Master
Hyperparameter Tuning with Grid and Random Search.This is a focused, practical course for building better machine learning models for tabular data.
In business, a model that performs well on one train-test split is not enough. You need reliable evidence that it will generalize to new customers, transactions, applications, or operational data. You also need a systematic way to improve performance without wasting time and computing resources on guesswork.
This course teaches you how to create robust cross-validation frameworks and use Grid Search and Random Search to optimize machine learning models with Python and scikit-learn.
Through concise explanations and hands-on coding demonstrations, you will learn how to
✨ Understand what hyperparameters are and why tuning them matters
✨ Evaluate models reliably using cross-validation
✨ Select an appropriate cross-validation strategy for your data
✨ Define practical hyperparameter search spaces
✨ Tune models systematically with Grid Search
✨ Explore larger search spaces efficiently with Random Search
✨ Compare Grid Search and Random Search and choose the right approach
✨ Tune popular models for tabular data, including XGBoost and LightGBM
✨ Apply the techniques to your own machine learning projects
This course is designed for data scientists, machine learning practitioners, analysts, and technical professionals who work with tabular data and want a practical, repeatable approach to model optimization.
Every topic is supported by hands-on Python examples that you can use for practice, reference, and adaptation in your own projects.
By the end of the course, you will be able to build a reliable model-validation framework, run effective hyperparameter searches, and make more confident model-selection decisions for real-world business applications.
Enroll today and learn how to move from trial-and-error tuning to a structured process for building more reliable, higher-performing machine learning models.Who this course is for⭐ Data scientists and machine learning practitioners who want a practical, focused guide to model evaluation and hyperparameter tuning.
⭐ Python and scikit-learn users who want to apply cross-validation correctly and confidently.
⭐ Beginners who understand the basics of machine learning and want to learn Grid Search and Random Search.
⭐ Practitioners who want to build more reliable models while avoiding common validation and tuning mistakes.
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