Scikit Learn Tutorial
Selected Reading
选读
- Scikit Learn - Discussion
- Scikit Learn - Useful Resources
- Scikit Learn - Quick Guide
- Dimensionality Reduction using PCA
- Clustering Performance Evaluation
- Scikit Learn - Clustering Methods
- Scikit Learn - Boosting Methods
- Randomized Decision Trees
- Scikit Learn - Decision Trees
- Classification with Naïve Bayes
- Scikit Learn - KNN Learning
- Scikit Learn - K-Nearest Neighbors
- Scikit Learn - Anomaly Detection
- Scikit Learn - Support Vector Machines
- Stochastic Gradient Descent
- Scikit Learn - Extended Linear Modeling
- Scikit Learn - Linear Modeling
- Scikit Learn - Conventions
- Scikit Learn - Estimator API
- Scikit Learn - Data Representation
- Scikit Learn - Modelling Process
- Scikit Learn - Introduction
- Scikit Learn - Home
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- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
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Scikit Learn - Home
Scikit Learn Tutorial
Scikit-learn (Sklearn) is the most useful and robust pbrary for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modepng including classification, regression, clustering and dimensionapty reduction via a consistence interface in Python. This pbrary, which is largely written in Python, is built upon NumPy, SciPy and Matplotpb.
Audience
This tutorial will be useful for graduates, postgraduates, and research students who either have an interest in this Machine Learning subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner.
Prerequisites
The reader must have basic knowledge about Machine Learning. He/she should also be aware about Python, NumPy, Scipy, Matplotpb. If you are new to any of these concepts, we recommend you take up tutorials concerning these topics, before you dig further into this tutorial.
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