Unsupervised Machine Learning : With 2 Capstone ML Projects

Crazy about Unsupervised Machine Learning?

This course is a perfect fit for you.

This course will take you step by step into the world of Unsupervised Machine Learning.

Unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets.

These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, cross–selling strategies, customer segmentation, and image recognition.

This course will give you theoretical as well as practical knowledge of Unsupervised Machine Learning.

This Unsupervised Machine Learning course is fun as well as exciting.

It will cover all common and important algorithms and will give you the experience of working on some real–world projects.

This course will cover the following topics:–

K Means Clustering

Hierarchical Clustering

DBSCAN Clustering

Evaluation Metrics for Clustering Analysis

Techniques used for Treating Dimensionality

Different algorithms for clustering

Different methods to deal with imbalanced data.

Correlation filtering

Variance filtering

PCA & LDA

t–SNE for Dimensionality Reduction

We have covered each and every topic in detail and also learned to apply them to real–world problems.

You will have lifetime access to the resources and we update the course regularly to ensure that its up to date.