Description
Unsupervised Deep Learning in Python: Find Structure in the Unknown
Most data has no labels—but that doesn’t mean it’s useless. Unsupervised learning uncovers hidden structures, reduces dimensionality, and powers recommendation engines, anomaly detection, and more. This course teaches you the most powerful unsupervised deep learning techniques used in industry and research.
What You’ll Build
- An autoencoder that compresses and reconstructs images
- A recommendation system using Restricted Boltzmann Machines (RBMs)
- A 2D visualization of high-dimensional data using t-SNE
- A dimensionality reducer with Principal Component Analysis (PCA)
- An anomaly detector for fraud or system failures
Key Techniques Covered
- Principal Component Analysis (PCA)—linear dimensionality reduction
- t-SNE—non-linear visualization of complex data
- Autoencoders—neural networks for compression and feature learning
- Restricted Boltzmann Machines (RBMs)—probabilistic models for collaborative filtering
- Clustering with deep features—K-means on learned representations
Real-World Applications
- E-commerce—product recommendations based on user behavior
- Cybersecurity—detecting anomalous network traffic
- Bioinformatics—clustering gene expression data
- NLP—topic modeling and semantic embeddings
Who Should Enroll?
- Data scientists tired of labeled-data dependency
- ML engineers building recommendation or anomaly systems
- Researchers exploring representation learning
- Students needing portfolio projects in unsupervised learning
Go Beyond Supervised Learning
Supervised learning is just the tip of the iceberg. This course equips you with the **tools to extract insight from unlabeled, raw, real-world data**—a critical skill in today’s data-rich, label-poor world.
Discover what your data is trying to tell you. Enroll today.
