🎯HyperParameter Tunning with Optuna Tutorial | TPE Sampler | Random Forest | Hindi скачать в хорошем качестве

🎯HyperParameter Tunning with Optuna Tutorial | TPE Sampler | Random Forest | Hindi

🎯 Complete Optuna Guide for Random Forest Tuning | All Samplers Visualized & Explained 💻 My Kaggle Profile: 🔗 https://www.kaggle.com/codingloading 💻 My GitHub Repository: 🔗 https://github.com/DataScientist00 📌 In this video, I walk you through a full end-to-end tutorial on using Optuna for hyperparameter tuning of a Random Forest classifier. We use the Pima Indians Diabetes Dataset from Kaggle and explore every major Optuna sampler — including TPE, Random, Grid and more — with clear visualizations and comparisons. 📊 Whether you're new to Optuna or looking to deepen your understanding of optimization strategies, this video covers everything from scratch — including code, visual insights, and practical tips for tuning real-world machine learning models. 📁 Dataset Source: 🔗 https://www.kaggle.com/datasets/uciml... 💻 Explore the Full Project Code: https://www.kaggle.com/code/codingloa... 🧠 What You’ll Learn: ✅ Optuna fundamentals and architecture ✅ Hyperparameter tuning for Random Forest (n_estimators, max_depth, etc.) ✅ In-depth explanation of all Optuna samplers:  • TPE Sampler  • Random Sampler  • Grid Sampler ✅ How to visualize Optuna studies using built-in plotting tools ✅ How different samplers behave and perform on the same dataset ✅ Real-world workflow using the diabetes dataset from Kaggle 🚀 Ideal For: 📈 Data Scientists 🧪 Machine Learning Practitioners 🛠 ML Engineers & MLOps Enthusiasts 📊 Anyone working on model optimization and automated tuning workflows 📌 Keywords for SEO: #Optuna #HyperparameterTuning #RandomForest #DiabetesPrediction #KaggleDataset #MachineLearning #MLOptimization #ModelTuning #AutoML #Python #Sklearn #CMAES #TPESampler #GridSearch #DataScience #Visualization #OptunaSamplers #MLProjects #FeatureEngineering #PimaDiabetes #KaggleProjects #CodeWithMe