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Our generation witnessed the boom of artificial intelligence and machine learning. This sector has revolutionized all technical spheres of the world. Python has always been an essential walk-through into machine learning. Knowing certain libraries is a must to get into the world of python in machine learning. Let us explore more about them.

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We are going to classify python libraries for machine learning into two-

Libraries for data pre processing and modelling
Libraries for visualization

Data Processing and Modeling

1. NumPy

NumPy is a free Python software library for numerical is a perfect tool for scientific computing and performing basic and advanced array operations. It stands for Numerical Python. It helps to process arrays that store values of the same data type and helps to perform math operations on arrays easily. It has high performance and minimum time of execution. Adding, slicing, multiplying, flattening, reshaping, and indexing the arrays are some of the basic array operations that can be performed using NumPy. It can also be used to perform advanced functions including stacking the arrays, splitting them into sections, broadcasting arrays, etc.

2. SciPy

SciPy is a free software library for scientific computing and technical computing on the data. It includes modules for linear algebra, optimization, integration, and statistics. Its main functionality was built upon NumPy, so that the arrays in numpy can make use of this library. It mainly aims at scientific programming and helps a lot in scientific programming projects. The extensive documentation helps us to easily play around with this library.

3. Pandas

Pandas is a library created to help developers work with data analysis and data handling. Pandas have various high-performance and easy-to-use data structures for manipulating data in the form of tables. It suits best for quick and easy data manipulation, data aggregation, reading, and writing the data as well as data visualization. Pandas can also take in data from different types of files such as CSV, excel etc. or a SQL database and create a Python object known as a data frame. A data frame can be used for data manipulation. Different operations such as join, merge, groupby, concatenate etc can easily be done on the dataframes.

There are 2 main data structures in python, namely,

Series: one-dimensional data
Data Frames: two-dimensional data

4. SciKit-Learn

Scikit-Learn is used by data scientists for handling standard machine learning tasks like clustering, regression, model selection, dimensionality reduction, and classification. It is built on top of other Python libraries like NumPy, SciPy, Matplotlib, Pandas, etc. and so it can be easily used along with these libraries. Both supervised and Unsupervised Machine learning can be implemented using this library.It is an industry-standard for data science projects written in Python.

Data Visualization

5. Matplotlib

Matplotlib is a standard data science library which helps to visualize our data using two-dimensional diagrams and graphs. Matplotlib is one of those plotting libraries that helps the data scientists to understand their models in detail. It provides an object-oriented API for embedding plots into the applications.

6. Seaborn

Seaborn is a library based on Matplotlib. It is an important machine learning tool for visualizing statistical models. It helps to summarize data and depict the overall distributions. When using this library, you get to benefit from an extensive gallery of visualizations. It is a high-level interface for creating beautiful and informative statistical graphics that are essential to exploring and understanding data.

The Top 10 Types of Data Visualization Made Simple – Boost Labs

Conclusion

Python has always offered essential support to data scientists in their projects. These python libraries have helped them ease their work. In order to step into the world of data science, it is necessary to know these libraries. Feel free to explore more.

Blog Contributed by- Nandana Sreeraj