Advanced Machine Learning Algorithms
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Advanced Machine Learning Algorithms
This course is part of Fractal Data Science Professional Certificate
Instructor: Analytics Vidhya
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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.
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There are 6 modules in this course
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.
In the fast-evolving field of machine learning, overfitting and underfitting are persistent challenges that can hinder the performance of models. The Regularization module delves deep into the techniques that address these challenges head-on. Over a span of 2 hours, learners will develop a profound understanding of how regularization techniques can enhance model generalization and robustness.
What's included
12 videos2 readings2 assignments1 programming assignment
12 videos•Total 62 minutes
- Introduction to the course•4 minutes
- Introduction to Problem Statement•3 minutes
- Division of the dataset•4 minutes
- Overfitting and Underfitting•5 minutes
- Introduction To The Apex Dataset•4 minutes
- L1 Regularization•9 minutes
- L2 Regularization•6 minutes
- Elastic Net Regularization•7 minutes
- Fine-Tuning Logistic Regression•4 minutes
- L1 Regularization •9 minutes
- L2 Regularization •3 minutes
- Elastic Net Regularization•4 minutes
2 readings•Total 20 minutes
- Syllabus - Advanced Machine Learning Algorithms•10 minutes
- Resources to be used in this module•10 minutes
2 assignments•Total 60 minutes
- Regularization in Linear Regression•30 minutes
- Check your understanding•30 minutes
1 programming assignment•Total 120 minutes
- Regularization Programming Assessment•120 minutes
In this module, learners will explore Bagging Algorithms, which are techniques that group models together for more accurate predictions. Learners will start by learning the basics of Bagging and why it's better. They will discover how these algorithms work and why bootstrapping is a powerful idea. Next, they will dive deeper into types of Bagging Algorithms. They will explore Random Forests, Extra Trees, and how to use Bagging with classifiers.
What's included
6 videos2 readings1 assignment1 programming assignment
6 videos•Total 28 minutes
- Understanding Ensemble Learning•4 minutes
- Introducing Bagging Algorithms•4 minutes
- Hands-on to Bagging Meta Estimator•7 minutes
- Introduction to Random Forest•5 minutes
- Understanding Out-Of-Bag Score•5 minutes
- Random Forest VS Classical Bagging VS Decision Tree•3 minutes
2 readings•Total 20 minutes
- Resources to be used in this module•10 minutes
- Extra Trees- Reading Material•10 minutes
1 assignment•Total 30 minutes
- Graded Assignment•30 minutes
1 programming assignment•Total 120 minutes
- Bagging Programming Assessment•120 minutes
In this module, learners will grasp the essence of boosting techniques and their transformative impact on model accuracy. The focus then shifts to AdaBoost, with an exploration of its underlying algorithm and the pivotal role it plays in boosting's iterative approach. Then, they will learn about Gradient Boosting Machines (GBM). The final lesson introduces learners to advanced boosting algorithm variants: XGBoost, LightGBM, and CatBoost.
What's included
6 videos1 reading1 assignment1 programming assignment
6 videos•Total 33 minutes
- Introduction to Boosting•5 minutes
- AdaBoost Step-by-Step Explanation •8 minutes
- Hands-on - AdaBoost•5 minutes
- Gradient Boosting Machines (GBM)•4 minutes
- Hands-on Gradient Boost•3 minutes
- Other Algo (XGBoost, LightBoost. CatBoost)•8 minutes
1 reading•Total 10 minutes
- Resources to be used in this module•10 minutes
1 assignment•Total 30 minutes
- Graded: Check Your Understanding•30 minutes
1 programming assignment•Total 180 minutes
- Boosting Assessment•180 minutes
What's included
10 videos1 reading2 assignments1 programming assignment
10 videos•Total 54 minutes
- Introduction to Feature Engineering and Hyperparameter Tuning•2 minutes
- Spliting the dataset•2 minutes
- Feature Transformation•7 minutes
- Feature Generation•10 minutes
- Feature Seletion•10 minutes
- Introduction to Hyperparameter and Grid Search CV•4 minutes
- Grid Search CV•7 minutes
- Random Search CV•4 minutes
- Bayesan Optimization•4 minutes
- Bayesian Optimization in synergix dataset•4 minutes
1 reading•Total 10 minutes
- Resources to be used in this module•10 minutes
2 assignments•Total 60 minutes
- Graded Assignment•30 minutes
- Check your understanding•30 minutes
1 programming assignment•Total 120 minutes
- Feature Engineering and Hyperparameter Tuning•120 minutes
This module, dedicated to 'Combining Models,' offers learners a concise yet insightful exploration into the realm of leveraging multiple models for superior performance. Learners will explore why mixing models is a great idea. They will delve into fundamental concepts of stacking, blending, and aggregation.
What's included
5 videos1 reading1 assignment1 programming assignment
5 videos•Total 24 minutes
- Introduction to the module•3 minutes
- Understanding Voting •4 minutes
- Leveraging the Voting•5 minutes
- Understanding Stacking ensemble learning•8 minutes
- Understanding Hold out Method/Blending•4 minutes
1 reading•Total 10 minutes
- Resources to be used in this module•10 minutes
1 assignment•Total 30 minutes
- Check your understanding•30 minutes
1 programming assignment•Total 180 minutes
- Stacking Programming Assessment•180 minutes
In this module, learners will dive into the important process of picking the right machine learning model for the job. The module begins by showing why choosing the right model matters. Learners will get to know about the factors they need to consider while choosing the model. They will get a handy guide that will help them in selecting the right model. They will learn about the essential things they need to look at while selecting a model, including performance metrics.
What's included
2 videos1 assignment
2 videos•Total 12 minutes
- The Stepping Stones in Model Selection•7 minutes
- Factors to Consider While Selecting a Model•5 minutes
1 assignment•Total 30 minutes
- Check your Understanding•30 minutes
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Reviewed on Jul 6, 2024
Very nice material with good explanation for each module
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