Advanced Machine Learning Algorithms (Coursera)

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.

This course is part of Fractal Data Science Professional Certificate.

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.

Syllabus

Getting Familiar with Regularisation
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.

Ensemble Learning - Bagging Algorithms
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.

Ensemble Learning - Boosting Algorithms
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.

Feature Engineering and Hyperparameter Tuning
This module navigates learners through the process of refining models for increased performance and precision. They will explore the critical roles that hyperparameter tuning and feature engineering play in model enhancement. They will delve into the significance of datetime features and the techniques to harness text data for improved predictions. Further, they will explore the strategies for optimizing models by carefully selecting features. They will master the art of leveraging techniques like grid search and random search to find optimal parameter configurations.

Combining Models
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.

Model Selection
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.