Скачать с ютуб Concrete Strength Prediction Using ML Libraries: Random Forest Regression using sklearn в хорошем качестве

Concrete Strength Prediction Using ML Libraries: Random Forest Regression using sklearn

ID - Video 42 In this video tutorial, we will guide you through the entire process of developing a machine learning model using scikit-learn, a powerful library renowned for its capabilities in implementing a wide range of machine learning algorithms. Specifically, we will focus on Random Forest Regression. From data import and organization to model building, training, and testing, we cover each step comprehensively. Learn how to visualize model predictions and save the trained model for future use, empowering you to make accurate predictions with ease. Don't miss out on this invaluable resource for anyone interested in machine learning and its real-world applications. Whether you're a beginner or an experienced practitioner, our tutorial provides insights and practical guidance to enhance your understanding of scikit-learn and its role in predicting concrete strength using Random Forest Regression. Subscribe to our channel for more exciting machine learning tutorials. Join us on this journey to unlock the potential of machine learning in civil engineering applications. Related Videos - 1.    • Project 4 - Concrete Strength Prediction U...   2.    • Part 1 of Project 4 - Machine Learning Mod...   3.    • Part 1 of Project 4 - Machine Learning Mod...   4.    • Part 2 of Project 4 - Concrete Strength Pr...   5.    • Concrete Compressive Strength Prediction U...   #machinelearning #sklearn #civilengineering #concretestructures #tensorflow #pytorch #regressionanalysis #randomforest