Unsupervised Machine Learning : With 2 Capstone ML Projects

• Understand the Working of K Means, Hierarchical, and DBSCAN Clustering.
• Implement K Means, Hierarchical, and DBSCAN Clustering using Sklearn.
• Learn Evaluation Metrics for Clustering Analysis.
• Learn Techniques used for Treating Dimensionality.
• Implement Correlation Filtering, VIF, and Feature Selection.
• Implement PCA, LDA, and t-SNE for Dimensionality Reduction.
• Analyze the Climatic Factors Best to Grow Certain Crops.
• Recommend Crops by looking at Certain Climatic Factors.
• Categorize the data into n number of relevant groups which are useful for Marketing Purposes.
• Identify the Target Group of Customers.
• Implement Soft K-Means Clustering in Code.
• Understand the limitations of PCA and t-SNE.
• Machine learning Concept and Different types of Machine Learning.

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:-

1. K Means Clustering

2. Hierarchical Clustering

3. DBSCAN Clustering

4. Evaluation Metrics for Clustering Analysis

5. Techniques used for Treating Dimensionality

6. Different algorithms for clustering

7. Different methods to deal with imbalanced data.

8. Correlation filtering

9. Variance filtering

10. PCA & LDA

11. 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.