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.