Hyperparameter Tuning of Machine Learning Models A MustWatch

>> YOUR LINK HERE: ___ http://youtube.com/watch?v=lEyzSXPAO-Q

Welcome to our deep dive into Hyperparameter Tuning, an essential process in optimizing the performance of your machine-learning models! • In this AMMnet Webinar, we explore hyperparameters' crucial role in model training and how tuning them can make the difference between a mediocre model and one that achieves outstanding results. Whether you're working with regression, classification, or any other type of predictive modeling, mastering hyperparameter tuning is key to maximizing the potential of your algorithms. • What You'll Learn: • What are Hyperparameters? Understand the difference between hyperparameters and model parameters, and why they matter. • Common Hyperparameters in Popular Algorithms: Learn about key hyperparameters in models like Decision Trees, Random Forests, SVMs, Neural Networks, and more. • Grid Search vs. Random Search:Discover two of the most widely used methods for hyperparameter tuning and how they compare. • Automated Tuning with Bayesian Optimization: Explore advanced techniques like Bayesian Optimization to streamline the tuning process. • Practical Walkthrough: Follow along with a hands-on example of tuning hyperparameters using 'caret' package in R. • 📈 Who Should Watch This Video? • Data scientists and machine learning practitioners looking to improve their model performance. • Beginners curious about the impact of hyperparameters on machine learning models. • Anyone interested in the nuances of model optimization techniques. • If you found this video helpful, please like, comment, and subscribe for more in-depth machine learning and data science tutorials. Hit the notification bell 🔔 to stay updated with my latest content! • #machinelearningplus #machinelearningengineer #datascienceacademy #AMMNet

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