Curriculum
- 7 Sections
- 58 Lessons
- 10 Weeks
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- Python & Data Tools for Machine Learning8
- Machine Learning Workflows & Data Prep8
- Supervised Learning – Classification8
- Supervised Learning – Regression9
- Unsupervised Learning – Clustering & Dimensionality Reduction13
- 5.1Clustering
- 5.2K-Nearest Neighbors for Unsupervised Tasks
- 5.3Elongated Clusters
- 5.4Gaussian Mixture Models (GMMs)
- 5.5Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
- 5.6Hierarchical Clustering
- 5.7Autoencoders (Neural Network-Based)
- 5.8Summary: Unsupervised Learning Algorithms
- 5.9t-Distributed Stochastic Neighbor Embedding (t-SNE)
- 5.10UMAP (Uniform Manifold Approximation and Projection)
- 5.11Apriori Algorithm
- 5.12Eclat Algorithm
- 5.13Spectral Clustering
- Model Evaluation & Tuning6
- Modern Topics – Transfer Learning, GANs, XAI6
Numpy and Pandas
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