HomeCoursesMastering Hyperparameter Optimization for Machine Learning

Intermediate

Updated 4 months ago

Mastering Hyperparameter Optimization for Machine Learning

Delve into hyperparameter optimization for machine learning models, exploring techniques like grid search, SMBO, TPE, and genetic algorithms using real-world datasets to enhance model performance.

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Overview

Content

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Machine learning models excel in classification, regression, anomaly detection, language translation, and more. Optimizing hyperparameters can enhance the performance of most machine learning models. This course will equip you with the skills to optimize hyperparameters for various machine learning models. You’ll begin with the introduction of hyperparameters and understand the need for optimizing them. Using a loan approval dataset for binary classification, you’ll explore both random and grid search methods for logistic regression and random forest models. Then, you’ll understand sequential model-based optimization (SMBO) and Tree-Structured Parzen Estimator (TPE), applying them to k-nearest neighbors (KNN) and histogram-based gradient boosting algorithms. You’ll finish by understanding and applying genetic algorithms to find the best hyperparameters for the KNN algorithm and random forest model. After completing this course, you’ll have gained skills to master the hyperparameter optimization.

Machine learning models excel in classification, regression, anomaly detection, language translation, and more. Optimizing hyper...Show More

WHAT YOU'LL LEARN

Familiarity with hyperparameter optimization methods, including random search, grid search, and sequential model-based optimization

Hands-on experience configuring, implementing, and evaluating hyperparameter optimization techniques using Python

Understanding the advantages and disadvantages of the various hyperparameter optimization methods

Working knowledge of Python libraries such as scikit-learn, TPOT, scikit-optimize, and Optuna for hyperparameter optimization

Familiarity with hyperparameter optimization methods, including random search, grid search, and sequential model-based optimization

TAKEAWAY SKILLS

Python

Data Science

Machine Learning

Content

37 Lessons1 Project5 Quizzes1 Assessment

Introduction

4 Lessons

Get familiar with hyperparameters, their optimization, and the dataset for machine learning models.

Course Overview

Introduction to Hyperparameters

Introduction to Hyperparameter Optimization

Introduction to the Dataset for the Course

Random Search Method

7 Lessons

Grasp the fundamentals of random search for hyperparameter optimization to enhance model performance.

Introduction to the Random Search Method

How Does Random Search Work

Data Preparation

Random Search Using Logistic Regression

Random Search Using the Random Forest Algorithm

Advantages and Disadvantages of Random Search Method

Quiz on Random Search Method

Grid Search Method

6 Lessons

Break apart the Grid Search method's steps, practical applications, and its pros and cons.

Introduction to Grid Search Method

How Does Grid Search Work

Grid Search Using Logistic Regression

Grid Search Using Random Forest Algorithm

Advantages and Disadvantages of the Grid Search Method

Quiz on Grid Search Method

Sequential Model-Based Optimization Method

6 Lessons

Apply your skills to optimize hyperparameters efficiently using Sequential Model-Based Optimization (SMBO).

Introduction to the Sequential Model-Based Optimization Method

How Does Sequential Model-Based Optimisation Work

Sequential Model-Based Optimization using K-Nearest Neighbors

SMBO Using Histogram-Based Gradient Boosting

Advantages and Disadvantages of the SMBO Method

Quiz on the Sequential Model-Based Optimization Method

Tree-Structured Parzen Estimators Method

6 Lessons

Explore the Tree-Structured Parzen Estimator method for enhancing hyperparameter optimization in machine learning.

Introduction to the Tree-Structured Parzen Estimator Method

How Does the Tree-Structured Parzen Estimator Method Work

Tree-Structured Parzen Estimator Method Using KNN

TSPE Method Using Histogram-Based Gradient Boosting

Advantages and Disadvantages of the TSPE Method

Quiz on the Tree-Structured Parzen Estimator (TPE) Method

Genetic Algorithm

6 Lessons

Follow the process of using genetic algorithms to optimize hyperparameters for machine learning models.

Introduction to the Genetic Algorithm

How Does the Genetic Algorithm Work

Genetic Algorithm Using the KNN Model

Genetic Algorithm Using Random Forest

Advantages and Disadvantages of the Genetic Algorithm

Quiz on Genetic Algorithm

Evaluate Hyperparameter Optimization Concepts

Assessment

Optimizing ML Model for Promotion Selection

Project

Conclusion

1 Lessons

Practice using hyperparameter optimization techniques in machine learning projects.

Final Remarks

Appendix

1 Lessons

Get familiar with installing Python packages using Anaconda for efficient environment management.

Set Up the Computer and Install Python Packages

Predict Cancer Using Machine Learning Models

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Davis David

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