ML Algorithms: Dimensionality Reduction

Explore the world of dimensionality reduction with our "Machine Learning Algorithms: Dimensionality Reduction" course. This course is designed to provide a deep understanding of various dimensionality reduction techniques and their applications in simplifying complex datasets. By the end, you'll be equipped to implement and evaluate multiple dimensionality reduction models to uncover patterns in data.

DIFFICULTY

Beginner to Intermediate

COURSE TYPE

SCHEDULE

Self-paced

PRE-REQUISITES

Basic knowledge of Python
and statistics recommended

What you'll learn

Introduction to
Machine Learning

Understand the basics of machine learning, including its types and real-world applications.

Data
Handling

Learn about data types, preprocessing techniques, and exploratory data analysis.

Dimensionality
Reduction Techniques

Dive deep into techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Autoencoders.

Model
Evaluation

Understand how to evaluate models using metrics like explained variance ratio and reconstruction error.

PCA Models

Develop models to transform high-dimensional data into lower-dimensional forms, retaining most of the original variance.

LDA Models

Learn to create models that maximize class separability while reducing dimensionality.

t-SNE Models

Implement t-SNE to visualize high-dimensional data in a two- or three-dimensional space.

Autoencoder Models

Build and train autoencoders to compress and reconstruct data efficiently.

Real-World Projects

Apply your knowledge to datasets like the Fashion MNIST dataset to visualize and interpret complex data.

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Frequently Asked Questions

What types of dimensionality reduction models will I learn in this course?

You will learn partition-based clustering, hierarchical clustering, density-based clustering, and advanced techniques like K-Means, DBSCAN, and Hierarchical Clustering.

Will this course cover data preprocessing techniques?

Yes, the course covers data preprocessing techniques such as handling missing values, encoding categorical variables, and normalizing data.

What kind of projects will I work on in this course?

You will work on projects like visualizing the Fashion MNIST dataset, compressing and reconstructing images using autoencoders, and exploring high-dimensional data with t-SNE.

How will I evaluate the performance of my dimensionality reduction models?

You will learn to evaluate models using metrics like explained variance ratio, reconstruction error, and visualization techniques to assess the effectiveness of dimensionality reduction.

Can I use autoencoders for tasks other than dimensionality reduction?

Yes, autoencoders can be used for tasks like denoising data, anomaly detection, and generating new data similar to the input data.