24. Hyperparameter Tuning with GridSearchCV – Find the Best Model Parameters! 🚀

Welcome back to the Machine Learning Classification series!

In this video, we’ll dive into Hyperparameter Tuning using GridSearchCV, a systematic approach to finding the best parameters for your machine learning model. Fine-tuning your model can boost performance and ensure optimal predictions!

What You’ll Learn:
✅ What is Hyperparameter Tuning, and why is it important?
✅ Difference between GridSearchCV and RandomizedSearchCV
✅ How to use GridSearchCV in Scikit-Learn
✅ Optimizing key parameters for RandomForestClassifier, Logistic Regression, and more
✅ Evaluating and selecting the best model configuration

By the end of this video, you'll be able to systematically tune hyperparameters to improve accuracy and generalization.

📌 Useful Links
📊 Dataset Links:
📌 Heart Disease Dataset 1: https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset
📌 Heart Disease Dataset 2: https://www.kaggle.com/datasets/deekshaa1/machine-learning-projects?select=heart_disease_data.csv

🗺️ Machine Learning Workflow Map:
📌 Scikit-Learn ML Map: https://scikit-learn.org/stable/machine_learning_map.html

👉 Stay Tuned: Subscribe for more hands-on ML tutorials, and hit the 🔔 notification bell to stay updated!

Looking for 1-on-1 Training?
I offer personalized training sessions on Machine Learning, BigQuery SQL, and Google Sheets Apps Script! Whether you're a beginner or looking to refine advanced skills, I’ve got you covered.

📩 Contact me at: iamrajivpujala@gmail.com

🔖 Hashtags for Better Reach:
#MachineLearning #HyperparameterTuning #GridSearchCV #DataScience #MLforBeginners #LearnMachineLearning #ScikitLearn #PythonProgramming #AI #DataAnalytics #ModelOptimization #CodingTutorial

Видео 24. Hyperparameter Tuning with GridSearchCV – Find the Best Model Parameters! 🚀 канала Code Craft with Rajiv Pujala