Machine Learning framework in Python
Features
- Support Vector Machines
- Cross Validation
- Generalized Linear Models
- Unsupervised learning
- Supervised learning
- Clustering
Categories
Machine LearningLicense
BSD LicenseFollow Scikit Learn
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Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications.
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User Reviews
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Excellent package, very full-featured with important algorithms. Also includes ways of generating data, cross validation, and grid search over parameters. Parallel processing is built-in for relevant algorithms.
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Fine work.
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+1