Top 5 Python libraries you must know for Machine Learning

Blog Eugenie May 09, 2019 | 2 min read
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Rising popularity of Python libraries

Machine Learning primarily involves algorithms which can learn from data as well as examples. The core idea of machine learning is to get better in learning/predicting with usage, without the specific instructions.

Python is the most popular language for programming machine learning algorithms. The reason behind the popularity of Python is the extensive machine learning libraries it offers. Libraries contain pre-written, reusable functions meant for specific usage to save time and efforts to write a new comprehensive code.

Python offers many libraries that make use of diverse mathematical functions such as statistics, probability, and optimization which are used frequently in machine learning based algorithms.

If you are a machine learning enthusiast, you must know the following Python libraries:

Pandas: It is a library used extensively for data manipulation and data analysis. It also provides time-series functionality which involves generation and conversion of dates for a given data set, manipulating dates for domain-specific time series data without any data loss. This library is highly optimized, and it is used in many domains such as economics, finance, statistics, web analytics, etc.

Numpy: Numpy is a widely popular python library used for scientific computations. It provides huge multidimensional arrays, derived objects such as matrices. It also includes support for mathematical concepts like linear algebra, mathematical logic, random number generations, basic statistical operations, and Fourier transform. Other computational activities such as sorting, shape manipulations, selecting, etc. are also supported in Numpy. Numpy is very fast and concise as it uses vectorized code which means code without looping, indexing, etc.

Matplotlib: Matpoltlib is a plotting library used for data visualizations. It is a 2D plotting library used to create bar charts, plots, histograms, scatterplots, error charts, etc. with minimal code. It is effective in scenarios where a pattern in the data set need to be visually represented. Matplotlib is popularly used in many scenarios such as generating PostScript files automatically, generating dynamic visual output in a web application, etc.

Scikit-learn: Scikit-learn provides support for data analysis and data mining. This library is reusable and accessible with support for supervised and unsupervised machine learning, model evaluation and selection, and dataset transformations. It is built on the NumPy, SciPy, and Matplotlib libraries.

Theano: Machine learning involves a wide usage of mathematical and statistical expressions. Theano is a robust library which is extensively used in large scale computations for defining, evaluating, and optimizing mathematical expressions with multi-dimensional arrays. It involves features like integration with NumPy, speed and stability optimization, unit testing and self-verification, etc.


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Eugenie

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