Unsupervised Learning with Python: Step-by-Step Tutorial!, Master advanced clustering, topic modeling, manifold learning, and autoencoders using Unsupervised Learning with Python!
- NEW
- Created by Packt Publishing
- English
- English [Auto-generated]
- 7.5 hours on-demand video
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
- Have a coupon?
What you'll learn
- Explore various Python libraries, including numpy, pandas, scikit-learn, matplotlib, seaborn and plotly
- Gain in-depth knowledge of Principle Component Analysis and use it to effectively manage noisy datasets.
- Discover the power of PCA and K-Means for discovering patterns and customer profiles by analyzing wholesale product data.
- Visualize, interpret, and evaluate the quality of the analysis done using Unsupervised Learning.
- Compare and evaluate the results of different data analyses to determine the quality of clusters, time, and memory usage.
- Compare T-SNE and UMAP with PCA and ICA, in the context of how different algorithms work and when to apply them.
- Evaluate the results of the analysis applied to various datasets using Unsupervised Learning.
Preview This Course - GET COUPON CODE
Post a Comment for "Unsupervised Learning with Python: Step-by-Step Tutorial!"