Learn how to build high-performance Machine Learning models with this practical course on Model Tuning and Optimization, a critical skill for every data scientist and AI engineer.
Even the best machine learning algorithms fail without proper tuning. This course teaches you how to optimize models for maximum accuracy and efficiency using industry-standard techniques.
You will start with an introduction to hyperparameter tuning, understanding the difference between parameters and hyperparameters and their impact on model performance.
Next, you’ll explore Grid Search and Random Search, the most commonly used techniques for systematically finding optimal model configurations.
Moving forward, you’ll dive into advanced hyperparameter tuning using Bayesian Optimization, a powerful method used in real-world AI systems to reduce computation time while improving results.
The course also covers regularization techniques, helping you prevent overfitting and build models that generalize well on unseen data.
You will then implement automated tuning using GridSearchCV and RandomizedSearchCV in Python, gaining hands-on experience with industry tools.
Finally, you’ll complete an end-to-end optimization project, where you will build, tune, and evaluate a final machine learning model on a real dataset.
Advanced Machine Learning Algorithms with Python
