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Download the complete Ensemble Methods for Machine Learning, Video Edition video course for free with high-definition MP4 video lectures, project exercise archives, and step-by-step masterclasses. Learn offline at your own pace with zero paywalls or recurring subscriptions.

55Lectures

11.8Total Hours

1563.3MB Total Size

MP4 / 1080p HD

Instructor: Senior Industry Specialist

English

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Chapter 1. Summary Ensemble Methods for Machine Learning, Video Edition

MP4 HD1m•2.96 MB

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Chapter 1. Our first ensemble Ensemble Methods for Machine Learning, Video Edition

MP4 HD7m•14.49 MB

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Chapter 1. Ensemble methods Hype or hallelujah Ensemble Methods for Machine Learning, Video Edition

MP4 HD10m•23.03 MB

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Chapter 1. Fit vs. complexity in individual models Ensemble Methods for Machine Learning, Video Edition

MP4 HD19m•42.11 MB

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Chapter 1. Terminology and taxonomy for ensemble methods Ensemble Methods for Machine Learning, Video Edition

MP4 HD5m•11.42 MB

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Chapter 1. Why you should care about ensemble learning Ensemble Methods for Machine Learning, Video Edition

MP4 HD8m•16.95 MB

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Chapter 2. Case study Breast cancer diagnosis Ensemble Methods for Machine Learning, Video Edition

MP4 HD13m•29.63 MB

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Chapter 2. Bagging Bootstrap aggregating Ensemble Methods for Machine Learning, Video Edition

MP4 HD19m•41.90 MB

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Chapter 2. Homogeneous parallel ensembles Bagging and random forests Ensemble Methods for Machine Learning, Video Edition

MP4 HD7m•14.96 MB

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Chapter 2. More homogeneous parallel ensembles Ensemble Methods for Machine Learning, Video Edition

MP4 HD7m•16.18 MB

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Chapter 2. Summary Ensemble Methods for Machine Learning, Video Edition

MP4 HD2m•5.34 MB

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Chapter 3. Case study Sentiment analysi Ensemble Methods for Machine Learning, Video Edition

MP4 HD17m•37.88 MB

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Chapter 2. Random forests Ensemble Methods for Machine Learning, Video Edition

MP4 HD10m•22.32 MB

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Chapter 3. Combining predictions by meta-learning Ensemble Methods for Machine Learning, Video Edition

MP4 HD16m•35.21 MB

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Chapter 3. Combining predictions by weighting Ensemble Methods for Machine Learning, Video Edition

MP4 HD19m•42.25 MB

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Chapter 3. Summary Ensemble Methods for Machine Learning, Video Edition

MP4 HD3m•7.68 MB

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Chapter 3. Heterogeneous parallel ensembles Combining strong learners Ensemble Methods for Machine Learning, Video Edition

MP4 HD17m•38.37 MB

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Chapter 4. AdaBoost Adaptive boosting Ensemble Methods for Machine Learning, Video Edition

MP4 HD23m•51.51 MB

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Chapter 4. AdaBoost in practice Ensemble Methods for Machine Learning, Video Edition

MP4 HD10m•21.11 MB

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Chapter 4. Case study Handwritten digit classification Ensemble Methods for Machine Learning, Video Edition

MP4 HD9m•19.79 MB

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Chapter 4. LogitBoost Boosting with the logistic loss Ensemble Methods for Machine Learning, Video Edition

MP4 HD7m•14.52 MB

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Chapter 4. Summary Ensemble Methods for Machine Learning, Video Edition

MP4 HD2m•4.55 MB

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Chapter 4. Sequential ensembles Adaptive boosting Ensemble Methods for Machine Learning, Video Edition

MP4 HD8m•18.08 MB

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Chapter 5. Case study Document retrieval Ensemble Methods for Machine Learning, Video Edition

MP4 HD11m•23.31 MB

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Chapter 5. LightGBM A framework for gradient boosting Ensemble Methods for Machine Learning, Video Edition

MP4 HD11m•23.36 MB

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Chapter 5. LightGBM in practice Ensemble Methods for Machine Learning, Video Edition

MP4 HD20m•43.41 MB

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Chapter 5. Gradient boosting Gradient descent + boosting Ensemble Methods for Machine Learning, Video Edition

MP4 HD25m•56.03 MB

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Chapter 5. Summary Ensemble Methods for Machine Learning, Video Edition

MP4 HD2m•5.44 MB

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Chapter 5. Sequential ensembles Gradient boosting Ensemble Methods for Machine Learning, Video Edition

MP4 HD28m•61.36 MB

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Chapter 6. Case study redux Document retrieval Ensemble Methods for Machine Learning, Video Edition

MP4 HD8m•17.26 MB

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Chapter 6. Sequential ensembles Newton boosting Ensemble Methods for Machine Learning, Video Edition

MP4 HD23m•50.42 MB

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Chapter 6. Summary Ensemble Methods for Machine Learning, Video Edition

MP4 HD3m•6.57 MB

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Chapter 6. Newton boosting Newton’s method + boosting Ensemble Methods for Machine Learning, Video Edition

MP4 HD19m•41.72 MB

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Chapter 6. XGBoost A framework for Newton boosting Ensemble Methods for Machine Learning, Video Edition

MP4 HD12m•25.39 MB

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Chapter 6. XGBoost in practice Ensemble Methods for Machine Learning, Video Edition

MP4 HD7m•15.96 MB

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Chapter 7. Case study Demand forecasting Ensemble Methods for Machine Learning, Video Edition

MP4 HD22m•48.15 MB

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Chapter 7. Learning with continuous and count labels Ensemble Methods for Machine Learning, Video Edition

MP4 HD42m•91.72 MB

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Chapter 7. Parallel ensembles for regression Ensemble Methods for Machine Learning, Video Edition

MP4 HD16m•35.29 MB

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Chapter 7. Summary Ensemble Methods for Machine Learning, Video Edition

MP4 HD3m•7.37 MB

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Chapter 7. Sequential ensembles for regression Ensemble Methods for Machine Learning, Video Edition

MP4 HD17m•36.90 MB

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Chapter 8. Case study Income prediction Ensemble Methods for Machine Learning, Video Edition

MP4 HD17m•36.92 MB

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Chapter 8. Encoding high-cardinality string features Ensemble Methods for Machine Learning, Video Edition

MP4 HD10m•21.97 MB

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Chapter 8. CatBoost A framework for ordered boosting Ensemble Methods for Machine Learning, Video Edition

MP4 HD12m•27.16 MB

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Chapter 8. Learning with categorical features Ensemble Methods for Machine Learning, Video Edition

MP4 HD33m•72.47 MB

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Chapter 8. Summary Ensemble Methods for Machine Learning, Video Edition

MP4 HD5m•10.42 MB

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Chapter 9. Case study Data-driven marketing Ensemble Methods for Machine Learning, Video Edition

MP4 HD9m•19.37 MB

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Chapter 9. Black-box methods for local explainability Ensemble Methods for Machine Learning, Video Edition

MP4 HD28m•62.46 MB

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Chapter 9. Black-box methods for global explainability Ensemble Methods for Machine Learning, Video Edition

MP4 HD22m•48.71 MB

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Chapter 9. Explaining your ensembles Ensemble Methods for Machine Learning, Video Edition

MP4 HD23m•50.97 MB

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Chapter 9. Summary Ensemble Methods for Machine Learning, Video Edition

MP4 HD7m•14.74 MB

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Chapter 9. Glass-box ensembles Training for interpretability Ensemble Methods for Machine Learning, Video Edition

MP4 HD17m•36.73 MB

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Part 1. The basics of ensembles Ensemble Methods for Machine Learning, Video Edition

MP4 HD1m•2.61 MB

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Part 2 Essential ensemble methods Ensemble Methods for Machine Learning, Video Edition

MP4 HD2m•4.43 MB

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Epilogue Ensemble Methods for Machine Learning, Video Edition

MP4 HD11m•25.12 MB

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Part 3. Ensembles in the wild Adapting ensemble methods to your data Ensemble Methods for Machine Learning, Video Edition

MP4 HD3m•7.28 MB

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What You'll Master in this Free Download Course

Classification, Regression, and Recommendation Methods

Sophisticated Implementations of Off-the-Shelf Ensembles

Random Forests, Boosting, and Gradient Boosting

Feature Engineering and Group Diversity

Interpretability and Explainability for Ensemble Methods

And…

About this Free Download Course

Ensemble Methods for Machine Learning, Video Edition is a course on improving the performance of machine learning models using Ensemble methods, published by Oreillly Online Academy. The course covers various techniques such as bundling, boosting, stacking, and voting, and shows how combining multiple models can lead to improved prediction accuracy and generalization. Learners will be introduced to popular ensemble algorithms such as Random Forests, AdaBoost, Gradient Boosting, and XGBoost. Through practical coding examples, participants will understand the theory behind these methods and how they are applied to real-world problems.You will discover core ensemble methods that have proven track records in both data science competitions and real-world applications. Practical case studies will show you how each algorithm works in production. Learners will gain a deep understanding of the theory behind Ensemble techniques and how they are applied to real-world machine learning problems. By the end of the course, learners will have a solid foundation in Ensemble methods, equipped with the skills to create high-performance models for complex machine learning tasks.

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