Machine Learning

5 courses

Mastering Dimensionality Reduction with Python

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.

See courses

This learning path includes:

5 courses with bite-sized lessons and practices

21 engaging lessons in text and video formats

72 hands-on practices

One-on-one guidance from Cosmo, our AI tutor

Verified skills you'll gain

INTERMEDIATE

Feature Engineering

DEVELOPING

Machine Learning Model Development

Tools you'll use

MatPlotLib

Numpy

Pandas

Python

Scikit-learn

TensorFlow

Trusted by learners working at top companies

4 lessons

14 practices

Navigating Data Simplification with PCA

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.

See details

3 lessons

11 practices

Linear Landscapes of Dimensionality Reduction

Unlock the secrets of Linear Discriminant Analysis (LDA) to improve your data's feature selection and enhance model accuracy through hands-on Python exercises.

See details

4 lessons

13 practices

Non-linear Dimensionality Reduction Techniques

Unravel the complexities of non-linear dimensionality reduction by mastering t-SNE, geared towards unveiling hidden patterns in multifaceted datasets.

See details

5 lessons

15 practices

Enigmatic Autoencoders for Dimensionality Reduction

In this course, explore how autoencoders can compress and reconstruct data, offering insights into unsupervised learning for dimensionality reduction.

See details

5 lessons

19 practices

Dimensionality Reduction with Feature Selection

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.

See details

Meet Cosmo: The smartest AI guide in the universe

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.

Whether you’re leveling up or just starting out, find your next step here.