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.
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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
Leading Data Science Education Platform Empowering Future Analytics Professionals
Analytics Vidhya, a subsidiary of Fractal Analytics, has established itself as a premier educational platform specializing in data science and machine learning education. The platform offers a comprehensive 28-week Data Science Immersive bootcamp with over 500 hours of live online instruction, alongside four core professional courses: Advanced Machine Learning Algorithms, Data Analysis Using SQL, Foundations of Machine Learning, and Insights of Power BI. The curriculum is designed to accommodate both beginners and professionals, with classes scheduled during weeknights and weekends to suit working professionals. Students receive hands-on experience through more than 20 industry projects, covering essential topics from Python programming and SQL to advanced concepts in machine learning and deep learning. The platform maintains a strong reputation with a 4.4/5 rating from student reviews, offering a 100% job placement guarantee and potential scholarships up to INR 20,000. The program's effectiveness is enhanced by its partnership with Fractal Analytics, a multinational artificial intelligence company, ensuring that the curriculum remains aligned with industry needs and current technological trends
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