Advanced Machine Learning Algorithms
- Offered byCoursera
Advanced Machine Learning Algorithms at Coursera Overview
Duration | 20 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Advanced Machine Learning Algorithms at Coursera Highlights
- Earn a certificate from Fractal Analytics
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Advanced Machine Learning Algorithms at Coursera Course details
- 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.
- In a world where data-driven solutions are revolutionizing industries, mastering advanced machine learning techniques is a pivotal skill that empowers innovation and strategic decision-making. This equips you with the expertise needed to harness advanced machine-learning algorithms. You will delve into the intricacies of cutting-edge machine-learning algorithms. Complex concepts will be simplified, making them accessible and actionable for you to harness the potential of advanced algorithms effectively. By the end of this course, you will learn to:
- 1. Employ regularization techniques for enhanced model performance and robustness.
- 2. Leverage ensemble methods, such as bagging and boosting, to improve predictive accuracy.
- 3. Implement hyperparameter tuning and feature engineering to refine models for real-world challenges.
- 4. Combine diverse models for superior predictions, expanding your predictive toolkit.
- 5. Strategically select the right machine learning models for different tasks based on factors and parameters.
Advanced Machine Learning Algorithms at Coursera Curriculum
Getting Familiar with Regularisation
Program Introduction
Introduction to the course
Introduction to Problem Statement
Division of the dataset
Overfitting and Underfitting
Introduction To The Apex Dataset
L1 Regularization
L2 Regularization
Elastic Net Regularization
Fine-Tuning Logistic Regression
L1 Regularization
L2 Regularization
Elastic Net Regularization
Resources to be used in this module
Graded Quiz: Check Your Understanding
Graded Quiz: Check Your Understanding
Regularization Programming Assessment
Ensemble Learning - Bagging Algorithms
Understanding Ensemble Learning
Introducing Bagging Algorithms
Hands-on to Bagging Meta Estimator
Introduction to Random Forest
Understanding Out-Of-Bag Score
Random Forest VS Classical Bagging VS Decision Tree
Resources to be used in this module
Extra Trees- Reading Material
Graded Quiz: Check Your Understanding
Bagging Programming Assessment
Ensemble Learning - Boosting Algorithms
Introduction to Boosting
AdaBoost Step-by-Step Explanation
Hands-on - AdaBoost
Gradient Boosting Machines (GBM)
Hands-on Gradient Boost
Other Algo (XGBoost, LightBoost. CatBoost)
Resources to be used in this module
Graded Quiz: Check Your Understanding
Boosting Assessment
Feature Engineering and Hyperparameter Tuning
Introduction to Feature Engineering and Hyperparameter Tuning
Spliting the dataset
Feature Transformation
Feature Generation
Feature Seletion
Introduction to Hyperparameter and Grid Search CV
Grid Search CV
Random Search CV
Bayesan Optimization
Bayesian Optimization in synergix dataset
Resources to be used in this module
Graded Quiz: Check Your Understanding
Graded Quiz: Check Your Understanding
Feature Engineering and Hyperparameter Tuning
Combining Models
Introduction to the module
Understanding Voting
Leveraging the Voting
Understanding Stacking ensemble learning
Understanding Hold out Method/Blending
Resources to be used in this module
Graded Quiz: Check Your Understanding
Stacking Programming Assessment
Model Selection
The Stepping Stones in Model Selection
Factors to Consider While Selecting a Model
Graded Quiz: Check Your Understanding
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