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Ensemble Techniques courses teach the strategies of combining multiple machine learning models to create a single and more accurate model. The courses deliver lectures on principles and techniques of ensemble methods, including bagging, boosting, and bootstrapping, and demonstrate implementation using machine learning libraries like Scikit-Learn and TensorFlow. Learners can compare and select appropriate models, practice different techniques through hands-on projects and evaluate ensemble model performance.
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Deakin University
24 Months · Online
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Ensemble techniques are machine learning algorithms that combine multiple models to produce better predictive performance than could be achieved from any of the constituent models alone. This is done by either combining the predictions from multiple models or training a new model to combine the predictions of the individual models. Examples of ensemble techniques include bagging, boosting, and stacking.
Ensemble methods in machine learning are techniques that combine multiple individual models to create a more robust predictive model. These techniques are used to improve models' accuracy, robustness, and interpretability. They are a popular choice for Data Scientists because they can improve prediction accuracy and reduce the risk of overfitting. Examples of ensemble methods include bagging, boosting, and stacking.
AdaBoost and XGBoost are two popular boosting algorithms used in machine learning. Boosting algorithms are used for supervised learning tasks such as classification and regression. Boosting algorithms are an ensemble method combining multiple weak learners to create a strong learner.
AdaBoost, or Adaptive Boosting, combines weak learners to create a strong learner. The AdaBoost algorithm creates a strong learner by giving more weight to the misclassified examples, thus forcing weak learners to focus on the problematic examples. The algorithm works by iteratively adding weak learners, for example, decision trees, and adjusting the weights of the weak learners to create a strong learner.
XGBoost, or Extreme Gradient Boosting, is a scalable, parallelized implementation of gradient boosting. It works by iteratively adding weak learners and updating the weights of the weak learners to create a strong learner. XGBoost is considered to be more accurate and faster than other boosting algorithms.
AdaBoost and XGBoost are powerful and popular boosting algorithms used in machine learning. Both are used for supervised learning tasks such as classification and regression. AdaBoost is a simple and effective algorithm that is easy to implement but may be prone to overfitting. XGBoost is a more robust algorithm that is faster and more accurate than other boosting algorithms.
Python programming for machine learning is a popular choice among software professionals, and many libraries and packages are available for ensemble methods. Popular packages include Scikit-Learn, XGBoost, and LightGBM. Each of these packages has its advantages and disadvantages, so it's essential to understand their differences.
The Python course on ensemble methods will cover the basics of ensemble learning, including combining different models, evaluating and tuning hyperparameters, and interpreting the results. It will also cover more advanced topics, such as stacking, bagging, and boosting. Finally, the course will provide hands-on experience with several popular Python packages for ensemble methods, including Scikit-Learn, XGBoost, and LightGBM.
Great Learning offers you an opportunity to learn Ensemble Models and Techniques through online courses with an upgraded syllabus from top universities. Register for these courses to enhance your knowledge and earn abilities to work with fundamental and advanced skills in Machine Learning and Artificial Intelligence. Elevate your competency in designing and building ensemble models to predict accurate outcomes, and gain PG certificates upon course completion.
Ensemble techniques are an essential part of artificial intelligence, combining multiple models' predictions with improving accuracy and performance. They are used in various predictive modeling applications, including classification, regression, and forecasting. Ensemble techniques can help reduce generalization errors, produce better models for complex datasets, and reduce overfitting. They can also reduce the time and resources needed to create a model by combining multiple models into one. Additionally, ensemble techniques can help identify the most essential features in a dataset, allowing data scientists to focus their efforts on the most critical aspects of the problem.
Job roles with skills in ensemble techniques include:
These are the popular PG courses to learn Ensemble Techniques:
Here is the list of universities and programs that teach Ensemble Techniques in their curriculum:
Here is the course list and fee details of the courses offering Ensemble Techniques,
PG Programs
Program Fee Details
PGP - Artificial Intelligence for Leaders
INR 1,70,000 + GST
USD 7800
Post Graduate Program in Artificial Intelligence
INR 3,35,000 + GST
PG Program in Data Science and Engineering
INR 3,50,000 + GST
PGP - Machine Learning
INR 1,25,000 + GST
Note: Please refer to the Fee Section on the program page for the updated fee details.
Here are the duration details of the Ensemble Techniques courses,
Program Duration Details
4 Months
24 Months
12 Months
5 Months
7 month
Note: Please refer to the Program Page for the updated details.
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