10 Data Science and Machine Learning Libraries

1. Pandas

Pandas is a Python package that provides fast, powerful, flexible and easy to use open source data analysis and manipulation and analysis tool,built on top of the Python programming language.
In particular, it offers data structures and operations for manipulating numerical tables and time series.

Uses of Pandas

Data Structure

Data cleansing

Data filling

Data normalization

Merges and joins

Data visualization

Statistical analysis

Data inspection

2. Numpy

NumPy is a library for the Python programming language, that offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms,multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Uses of Numpy

Vector-Vector,Matrix-Matrix and Matrix-Vector multiplication

Reduction, statistics

Element-wise or array-wise comparisons

Linear Algebra operations

Bitwise Operators

Linear Algebra

Copying and viewing arrays

Stacking

3. SciPy

SciPy is a Python-based ecosystem of open-source software for mathematics,scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Uses of scipy

Optimization

Linear algebra

Integration

Interpolation

Signal and Image processing

4. Matplotlib

Uses of Matplotlib

Histograms, Spectrograms

2D plots

Line Plot,Scatter Plot

Bar Chart,Pie Chart

5. Seaborn

Seaborn is a Python data visualization library based on matplotlib. Many data scientists prefer seaborn over matplotlib due to its high-level interface for drawing attractive and informative statistical graphics.

Uses of Seaborn

Distribution Plots

Pie Chart & Bar Chart

Scatter Plots

Pair Plots

Heat maps

6. TensorFlow

TensorFlow is an open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.

Uses of Tensorflow

Voice/Sound Recognition

Classification, Perception

Understanding

Discovering

Prediction and Creation.

7. Scikit Learn

Scikit-learn is a free software machine learning and data analysis library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines

Uses of Scikit Learn

Regression and clustering

Model selection

Dimensionality reduction

Ensemble methods

Feature extractionFeature selection

Parameter Tuning

Manifold Learning

Supervised Models

8. PyTorch

PyTorch is an source machine learning framework that accelerates the path from research prototyping to production deployment. it is used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab.

Uses of PyTorch

Distributed Training

Robust Ecosystem

Cloud support

Production Ready

9. Keras

Uses of Keras

High-level neural networks AP

Multi-GPU & distributed training

Activation, and cost functions

TensorFlow Cloud Support

10. PyCaret

PyCaret is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of notebook environment.

Uses of PyCaret

Building ensemble models

Encoding categorical data,

Feature engineering

Hyperparameter tuning

Model deployment.

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