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Working in a hands-on learning environment, led by our Ensemble Machine Learning Cookbook expert instructor, students will learn about and explore:
Apply popular machine learning algorithms using a recipe-based approach.Implement boosting, bagging, and stacking ensemble methods to improve machine learning models.Discover real-world ensemble applications and encounter complex challenges in Kaggle competitions.
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Last Updated
July 29, 2021
Students Enrolled
20
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Certification
Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This course will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The course will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding lessons, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this course, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.
Course Curriculum
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- Get Closer to Your Data 00:00:00
- Introduction 00:00:00
- Data manipulation with Python 00:00:00
- Analyzing, visualizing, and treating missing values 00:00:00
- Exploratory data analysis 00:00:00
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- Getting Started with Ensemble Machine Learning 00:00:00
- Introduction to ensemble machine learning 00:00:00
- Max-voting 00:00:00
- Averaging 00:00:00
- Weighted averaging 00:00:00
- Resampling Methods 00:00:00
- Introduction to sampling 00:00:00
- k-fold and leave-one-out cross-validation 00:00:00
- Bag the Models with Bagging 00:00:00
- Introduction 00:00:00
- Bootstrap aggregation 00:00:00
- Ensemble meta-estimators 00:00:00
- Bagging regressors 00:00:00
- Boosting Model Performance with Boosting 00:00:00
- Introduction to boosting 00:00:00
- Implementing AdaBoost for disease risk prediction using scikit-learn 00:00:00
- Implementing a gradient boosting machine for disease risk prediction using scikit-learn 00:00:00
- Implementing the extreme gradient boosting method for glass identification using XGBoost with scikit-learn 00:00:00
- Homogeneous Ensembles Using Keras 00:00:00
- Introduction 00:00:00
- An ensemble of homogeneous models for energy prediction 00:00:00
- An ensemble of homogeneous models for handwritten digit classification 00:00:00
- Heterogeneous Ensemble for Text Classification Using NLP 00:00:00
- Introduction 00:00:00
- Spam filtering using an ensemble of heterogeneous algorithms 00:00:00
- Sentiment analysis of movie reviews using an ensemble model 00:00:00
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