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:
Advanced Machine Learning Algorithms
Advanced Machine Learning Algorithms
This course is part of Fractal Data Science Professional Certificate
Instructor: Analytics Vidhya
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
20 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
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.
Details to know
Earn a career certificate
Add to your LinkedIn profile
Assessments
8 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
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This course is part of the Fractal Data Science Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Fractal Analytics
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Share it on social media and in your performance review
There are 6 modules in this course
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
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
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
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.
What's included
10 videos1 reading2 assignments1 programming assignment
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
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
Instructor
Offered by
Recommended if you're interested in Data Analysis
Fractal Analytics
Fractal Analytics
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