This comprehensive learning path teaches Python-based dimensionality reduction, a key skill in data science and machine learning. By the end, you will master techniques to extract essential features from high-dimensional data, boosting model efficiency.
This learning path includes:
5 courses with bite-sized lessons and practices
21 engaging lessons in text and video formats
72 hands-on practices in our state-of-the art IDE
One-on-one guidance from Cosmo, our AI tutor
Grasp the essentials of dimensionality reduction and lay the groundwork for your journey by understanding and implementing Principal Component Analysis (PCA) using Python's Scikit-learn. This launchpad course provides a comprehensive introduction into why, how and when to use PCA for feature extraction and enhancing computational efficiency in high-dimensional data sets.
Unlock the secrets of Linear Discriminant Analysis (LDA) to improve your data's feature selection and enhance model accuracy through hands-on Python exercises.
Unravel the complexities of non-linear dimensionality reduction by mastering t-SNE, geared towards unveiling hidden patterns in multifaceted datasets.
In this course, explore how autoencoders can compress and reconstruct data, offering insights into unsupervised learning for dimensionality reduction.
In this course, you'll learn specialized techniques for feature selection and extraction to improve machine learning models. Through practical applications on a synthetic dataset, you'll discover how to identify and remove low-variance features, use correlation with the target variable, and apply advanced selection methods to refine your datasets for optimal efficiency and effectiveness.
Our built-in AI guide and tutor, Cosmo, prompts you with challenges that are built just for you and unblocks you when you get stuck.