Hyperparameter Tuning in Machine Learning: Techniques to Optimize Your Model
Hyperparameter tuning is a critical step in building machine learning models that perform at their best. In this video, we’ll demystify hyperparameter tuning, covering the key techniques to optimize your model’s performance and improve its accuracy.
This video is a part of our Intro To Machine Learning course: https://bit.ly/3VFrVrO
What You’ll Learn:
What hyperparameters are and why they matter in machine learning
Popular techniques for hyperparameter tuning
How to use libraries like scikit-learn and TensorFlow for tuning
Real-world examples of improving model performance through tuning
Whether you’re new to machine learning or looking to refine your optimization skills, this tutorial will introduce you to practical insights and tools to tune your models effectively.
About The Instructor: Matt Maybeno is a Principal Software Engineer at SOCi. With a masters in Bioinformatics from SDSU, he utilizes his cross domain expertise to build solutions in NLP and predictive analytics.
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