This course provides an in-depth understanding of hyperparameter tuning techniques, focusing on Grid Search and Random Search methods. Participants will learn how to optimize machine learning models by effectively selecting hyperparameters to improve performance.
Course Levels
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Level 1: Introduction to Hyperparameters
Get acquainted with the fundamental concepts of hyperparameters, their role in machine learning models, and the importance of tuning them.
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Level 2: Basics of Grid Search
Dive into the Grid Search method for hyperparameter tuning, understanding its mechanics and implementation.
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Level 3: Basics of Random Search
Explore the Random Search method, its advantages, and how it differs from Grid Search.
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Level 4: Advanced Tuning Techniques
Learn about advanced techniques and strategies to enhance hyperparameter tuning beyond Grid and Random Search.
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Level 5: Practical Applications
Apply the learned techniques through practical examples and projects to solidify understanding.
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Level 6: Tools and Libraries
Discover the tools and libraries available for hyperparameter tuning, focusing on their features and usage.
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Level 7: Capstone Project
Complete a comprehensive capstone project that incorporates all the knowledge gained throughout the course.
Course Topics
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Case Study: Tuning a Neural Network
# Case Study: Tuning a Neural Network In this section, we will dive deep into a case study that illustrates the practical application of hyperparameter tuning in neural networks. Specifically, we wil...
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Using Optuna for Advanced Tuning
# Using Optuna for Advanced Tuning In the realm of hyperparameter optimization, Optuna has emerged as a powerful tool that offers a more sophisticated approach than traditional grid or random search ...
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Evaluating Model Performance
# Evaluating Model Performance Evaluating model performance is a crucial step in the machine learning workflow. It allows us to understand how well our model performs and whether it meets the require...
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Project Introduction and Requirements
# Project Introduction and Requirements In any data science project, particularly in the realm of hyperparameter tuning, it is crucial to establish a solid foundation from the outset. This phase sets...
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Common Hyperparameters in Machine Learning
# Common Hyperparameters in Machine Learning In the realm of machine learning, hyperparameters play a crucial role in shaping the behavior of algorithms. Unlike model parameters, which are learned fr...
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Hyperparameter Tuning with TPOT
# Hyperparameter Tuning with TPOT Hyperparameter tuning is a critical step in the machine learning workflow that involves selecting the best parameters for a model to optimize its performance. TPOT (...
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Applying Grid and Random Search
# Applying Grid and Random Search Hyperparameter tuning is a crucial step in building robust machine learning models. This topic focuses on two prevalent techniques: Grid Search and Random Search. Bo...
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Introduction to Grid Search
# Introduction to Grid Search Grid Search is a powerful and systematic method for hyperparameter tuning in machine learning models. It involves exhaustively searching through a specified subset of hy...
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Bayesian Optimization for Hyperparameter Tuning
# Bayesian Optimization for Hyperparameter Tuning Bayesian Optimization is a powerful strategy for hyperparameter tuning, particularly when dealing with expensive evaluation functions. In contrast to...
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Implementing Random Search in Python
# Implementing Random Search in Python Random Search is a powerful technique for hyperparameter tuning in machine learning. Unlike Grid Search, which exhaustively tests all combinations of hyperparam...
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What are Hyperparameters?
# What are Hyperparameters? Hyperparameters are the parameters in machine learning algorithms that are not learned from the data but are set before the training process begins. Unlike model parameter...
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Random Search Algorithm Explained
# Random Search Algorithm Explained In the field of machine learning, hyperparameter tuning is a crucial step that can significantly influence the performance of a model. Among various techniques use...
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Using Cross-Validation with Hyperparameter Tuning
# Using Cross-Validation with Hyperparameter Tuning Hyperparameter tuning is crucial for improving the performance of machine learning models. However, simply adjusting hyperparameters may lead to ov...
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Comparing Random Search and Grid Search
# Comparing Random Search and Grid Search Hyperparameter tuning is a crucial step in building machine learning models, as it can significantly affect the performance of the model. Two popular methods...
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Performance Metrics Evaluation for Tuned Models
# Performance Metrics Evaluation for Tuned Models In the realm of machine learning, hyperparameter tuning is an essential step to enhance model performance. Once we have tuned our models using techni...
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Setting Up a Grid Search in Python
# Setting Up a Grid Search in Python Grid search is a powerful technique for hyperparameter tuning in machine learning. In this section, we'll learn how to set up a grid search using Python, specific...
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Leveraging Keras Tuner for Deep Learning
# Leveraging Keras Tuner for Deep Learning ## Introduction In the realm of deep learning, hyperparameter tuning is crucial for optimizing model performance. Keras Tuner is a powerful library designed...
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Hyperband and Successive Halving
# Hyperband and Successive Halving Hyperband and Successive Halving are advanced hyperparameter tuning techniques that efficiently allocate resources to evaluate configurations in a budget-conscious ...
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When to Use Random Search
# When to Use Random Search Random Search is a hyperparameter tuning technique that randomly samples from a predefined hyperparameter space, rather than exhaustively searching through it as in Grid S...
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Limitations of Grid Search
# Limitations of Grid Search Grid Search is a popular technique for hyperparameter tuning in machine learning models, allowing practitioners to systematically explore combinations of hyperparameters....
- And 15 more topics...