Introduction to Scikit-Learn: The First Step into MachineLearning

If you are a Python programmer or you are looking for a robust library you can use to bring machine learning into a production system then a library that you will want to seriously consider is scikit-learn.

Scikit-learn is an open-source Python library that implements a range of machine learning, preprocessing, cross-validation, and visualization algorithms using a unified interface.

What is scikit-learn?

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.

It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.

The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn. This stack includes:

• NumPy: Base n-dimensional array package
• SciPy: Fundamental library for scientific computing
• Matplotlib: Comprehensive 2D/3D plotting
• IPython: Enhanced interactive console
• Sympy: Symbolic mathematics
• Pandas: Data structures and analysis

Deciding an algorithm to proceed? Check this out!

The different modules that are included in the Scikit learn are:

• Clustering: for grouping unlabeled data such as KMeans.
• Cross-Validation: for estimating the performance of supervised models on unseen data.
• Datasets: for test datasets and for generating datasets with specific properties for investigating model behavior.
• Dimensionality Reduction: for reducing the number of attributes in data for summarization, visualization, and feature selection such as Principal component analysis.
• Ensemble methods: for combining the predictions of multiple supervised models.
• Feature extraction: for defining attributes in image and text data.
• Feature selection: for identifying meaningful attributes from which to create supervised models.
• Parameter Tuning: for getting the most out of supervised models.
• Manifold Learning: For summarizing and depicting complex multi-dimensional data.
• Supervised Models: a vast array not limited to generalized linear models, discriminate analysis, naive Bayes, lazy methods, neural networks, support vector machines, and decision trees.

Decision tree

Random Forest Classifier

A Basic Example:

An example code snippet:

Who is using it?

The scikit-learning-lists Inria, Mendeley, wise.io , Evernote, Telecom,JP-Morgan, ParisTech and AWeber as users of the library.

If this is a small indication of companies that have presented on their use, then there are very likely tens to hundreds of larger organizations using the library.

It has good test coverage and managed releases and is suitable for prototype and production projects alike.

Conclusion

Overall the Scikit-Learn library is a very good place to start off with your machine learning journey, and having a very strong hold over the library would enable us to grasp and understand more demanding topics when we delve deep into the machine learning topics