Python Libraries for Machine Learning

Numerous data processing and machine learning related libraries make the python programming language best choice for machine learning projects. Here is the list of python libraries I want you should include in your machine learning toolkit.

Numpy

Providing support for large, multi-dimensional arrays and matrices, along with large collection of functions to operate on these arrays.

Pandas

Supports data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series.

Matplotlib

It is 2D ploting library. Data visualization is crucial to better understand the data. It works well with Numpy.

Scikit-learn

It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting and k-means, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Tensorflow

TensorFlow is a low-level library that allows you to build machine learning models (and other computations) using a set of simple operators, like “add”, “matmul”, “concat”, etc. scikit-learn is a higher-level library that includes implementations of several machine learning algorithms.

NLTK

It is a collection of sample data, libraries and programs for symbolic and statistical natural language processing for English. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities.