5. Supervised Learning – Advanced Algorithms

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Navigate through the learning materials and practice exercises.

1. 5

   Supervised Learning – Advanced Algorithms

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   This section explores advanced supervised learning algorithms that enhance...

2. 5.1

   Overview Of Advanced Supervised Learning

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   This section introduces advanced supervised learning algorithms that enhance...

3. 5.2

   Support Vector Machines (Svm)

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   Support Vector Machines (SVM) are powerful supervised learning algorithms...

4. 5.2.1

   Concept

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   Support Vector Machines (SVM) are advanced supervised learning algorithms...

5. 5.2.2

   Kernel Trick

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   The Kernel Trick is a technique used in Support Vector Machines (SVM) that...

6. 5.2.3

   Pros And Cons

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   This section outlines the advantages and disadvantages of Support Vector...

7. 5.3

   Ensemble Learning

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   Ensemble learning combines predictions from multiple models to improve...

8. 5.3.1

   What Is Ensemble Learning?

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   Ensemble learning combines predictions from multiple base models to enhance...

9. 5.3.2

   Random Forest

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   Random Forest is an ensemble learning method that builds multiple decision...

10. 5.3.3

    Gradient Boosting Machines (Gbm)

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    Gradient Boosting Machines (GBM) are sequential ensemble models that focus...

11. 5.4

    Extreme Gradient Boosting (Xgboost)

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    XGBoost is a powerful and efficient implementation of gradient boosting that...

12. 5.4.1

    Introduction

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    This section introduces supervised learning and its significance, focusing...

13. 5.4.2

    Features

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    This section covers the key features of XGBoost, highlighting its unique...

14. 5.4.3

    Applications

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    This section details the practical applications of XGBoost in various fields.

15. 5.5

    Lightgbm And Catboost

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    LightGBM and CatBoost are advanced algorithms designed to enhance gradient...

16. 5.5.1

    Lightgbm

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    LightGBM is a gradient boosting framework that uses tree-based learning...

17. 5.5.2

    Catboost

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    CatBoost is an advanced gradient boosting algorithm optimized for...

18. 5.6

    Neural Networks

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    Neural Networks are composed of multiple layers that process data through...

19. 5.6.1

    Structure

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    This section introduces the structure of neural networks, detailing their...

20. 5.6.2

    Use Cases

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    This section highlights the practical applications of neural networks in...

21. 5.6.3

    Deep Learning Vs Traditional Ml

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    Deep learning offers automated feature extraction and handles large...

22. 5.7

    Automl And Hybrid Models

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    This section discusses AutoML and hybrid models, which automate model...

23. 5.7.1

    Automl

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    AutoML simplifies the process of model selection, hyperparameter tuning, and...

24. 5.7.2

    Hybrid Models

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    Hybrid models combine deep learning with structured machine learning...

25. 5.8

    Model Evaluation Techniques

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    This section covers essential techniques for evaluating supervised learning...

26. 5.9

    Hyperparameter Tuning

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    Hyperparameter tuning is crucial in optimizing machine learning model...

27. 5.10

    Deployment Considerations

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    Deployment considerations involve critical aspects such as model size,...