Advanced Machine Learning Algorithms
Learn regularization, ensemble methods, and hyperparameter tuning to refine models for real-world challenges and improve predictive accuracy.
Learn regularization, ensemble methods, and hyperparameter tuning to refine models for real-world challenges and improve predictive accuracy.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Fractal Data Science Professional Certificate program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Employ regularization techniques for enhanced model performance and robustness
Leverage ensemble methods, such as bagging and boosting, to improve predictive accuracy
Implement hyperparameter tuning and feature engineering to refine models for real-world challenges
Combine diverse models for superior predictions, expanding your predictive toolkit
Strategically select the right machine learning models for different tasks based on factors and parameters
Master advanced algorithms like Random Forest, XGBoost, and AdaBoost for practical applications
Skills you'll gain
This course includes:
20 Hours PreRecorded video
8 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 6 modules in this course
This comprehensive course delves into advanced machine learning algorithms, equipping learners with essential techniques to enhance model performance and accuracy. Beginning with regularization methods to combat overfitting, the curriculum progresses through ensemble learning techniques including bagging and boosting algorithms. Students master the implementation of random forests, AdaBoost, gradient boosting, and variants like XGBoost and LightGBM. The course also covers critical skills in feature engineering and hyperparameter tuning to optimize model performance. Learners gain practical experience through hands-on programming assignments, learning to combine models through stacking and blending, and developing strategic frameworks for selecting the most appropriate algorithms for specific use cases.
Getting Familiar with Regularisation
Module 1 · 4 Hours to complete
Ensemble Learning - Bagging Algorithms
Module 2 · 3 Hours to complete
Ensemble Learning - Boosting Algorithms
Module 3 · 4 Hours to complete
Feature Engineering and Hyperparameter Tuning
Module 4 · 4 Hours to complete
Combining Models
Module 5 · 4 Hours to complete
Model Selection
Module 6 · 42 Minutes to complete
Fee Structure
Instructor
Diverse, Industry-Focused AI and Data Science Courses
Fractal Analytics, as a leading Coursera partner, provides a comprehensive suite of online courses and professional certificates in English, covering topics such as Generative AI, data science, human behavior, coding, data storytelling, responsible AI, and quantum computing. Their offerings include foundational and advanced programs like "GenAI for Everyone," "Coding with Generative AI," "Data Frameworks for Generative AI," and "Python for Data Science," catering to learners from various backgrounds and industries. These courses are designed for continuous learning, blending instructor-led, eLearning, and blended formats, and aim to equip professionals and organizations with in-demand skills for the evolving digital economy, while also supporting career advancement through recognized certifications and practical, hands-on projects
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