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Master Hyperparameter Tuning with Grid and Random Search
Published 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
Learn cross-validation and hyperparameter tuning for tabular models, including XGBoost and LightGBM.
What you'll learn
Why tune hyperparamaters Cross-validation frameworks Grid search Random searchRequirements
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 metricsDescription
Welcome to MasterHyperparameter 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 projectsThis 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.Homepage
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