Top 5 Favorite Data Science Libraries
By: Pavan Kalyan
Tags: Artificial Intelligence, Keras, Machine Learning, Data Science, Python Libraries

5. Tensor Flow

Created by the Google Brain Team, TensorFlow is an open-source library for numerical computation that makes Machine Learning easier and faster. Helps developers to build and train state-of-the-art models without compromising in speed or performance, also create large-scale neural networks using data flow graphs. It can handle various tasks such as speech recognition and object recognition etc.

Key features:

Works efficiently with mathematical expressions involving multi-dimensional arrays.

Best support of Deep Neural Networks and Machine Learning Tasks.

The same code can be executed on both GPU/CPU architectures.

High scalability of computation across various machines and huge datasets.

Used for Text-based applications, Video detection, Speech, and image recognition.

To learn more about TensorFlow go through the link below:

https://www.tensorflow.org/tutorials/

4. Keras

Developed by Francois Chollet, Keras is an open-source library that provides a Python interface for Neural Networks. Runs on top of Libraries such as TensorFlow, Theano, Cognitive Toolkit (CNTK). Leverages several optimization techniques to make high-level neural network API simple and performant.

Key features:

Simple, Consistent, and extensible API.

Supports multiple platforms and backends.

A user-friendly framework that runs on both CPU and GPU.

Minimal structure, easy to achieve the results without any fail.

To learn more about Keras check this out https://keras.io/

3. Pandas

Developed by Wes McKinney, Pandas is a high-level data manipulation tool that is powerful, flexible, and easy to use open-source data analysis, operations for manipulating numerical tables and time series. Pandas allow converting data structures to Data Frame objects, handling missing data, and adding/removing columns from Data Frame also used to plot the data in histogram or box plot, etc.

Key features:

Consists of high-level data structures and manipulation tools.

Allows you to create functions and run it across a series of data.

Rich functionalities offer you the freedom to deal with missing data.

Helps to read data from different file formats.

Useful for data wrangling and cleaning.

Support Time-series functionalities, such as date range generation, moving window, and date shifting.

To know more about Pandas in 10 minutes, have a look at this —

https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html

2. Matplotlib

Matplotlib is my favorite and one of the most popular packages used for data visualization. It also provides an Object-oriented API that helps in embedding plots in applications. I wouldn’t say that it’s easy to use, but if you save a few most commonly used code blocks for basic charts and scatter plots i.e. pretty easy to create charts faster.

Photo by Clay Banks on Unsplash

Key features:

Support dozens of backends and output types, regardless of which operating system you are using.

Good run time behavior and low memory consumption.

Usable as a MATLAB replacement and also support various types of plots.

Used for correlation analysis of variables and visualize the distribution of data to get better insights.

For more knowledge visit https://matplotlib.org/

1. Scikit Learn

Developed by David Cournapeau as Google summer of code project. Scikit Learn is a free Machine Learning Library for python. It features algorithms such as Naive Bayes, Gradient Boosting, Random Forest, SVM, and also supports numerical and scientific libraries like NumPy, SciPy.

Features:

Clustering helps in grouping unlabeled data such as K-means.

Cross-Validation is used to estimate the performance of supervised models on unseen data.

Ensemble methods were able to combine the results of multiple supervised models.

Feature selection is used to identify important attributes used to create better models.

Hyper-parameter tuning improves the performance of algorithms.

To explore in-depth visit here https://scikit-learn.org/stable/