Book Description
Unsupervised machine learning is revolutionizing how organizations extract value from raw data, revealing patterns and structures without predefined labels. From customer segmentation and fraud detection to generative modeling, its versatility drives innovation across industries.

Kickstart Unsupervised Machine Learning is your comprehensive companion to mastering this transformative field. Starting with the core principles, the book introduces essential clustering algorithms—including K-Means, DBSCAN, and hierarchical approaches—before advancing to dimensionality reduction techniques such as PCA, t-SNE, and UMAP for simplifying complex data. It then explores sophisticated models like Gaussian Mixture Models and Generative Adversarial Networks (GANs), combining theory with practical coding exercises and hands-on projects using real-world datasets to solidify your understanding.

Thus, by the end of this book, you will confidently evaluate, deploy, and optimize unsupervised models to derive meaningful insights from unstructured data.

Table of Contents
1. Understanding Unsupervised Learning
2. Python Basics for Machine Learning
3. Clustering Techniques
4. Dimensionality Reduction
5. Anomaly and Outlier Detection
6. Deep Unsupervised Learning
7. Applications of Unsupervised Learning
8. Unsupervised Learning for Natural Language Processing
9. Evaluating Unsupervised Learning Models
10. Deploying Unsupervised Learning Models into Production
11. Case Studies and Best Practices in Unsupervised Learning
Index