Python is a go-to-go coding language for all those who wish to give their life to machines. Python is the top-most programming language for ML and AI.
The machine learning python library has modules for developers so that they can know what their requirements are. If you have not worked with machine learning closely, it will be very time-consuming to search and find python machine learning libraries for you. You should be familiar with python to know which modules you need for your project.
Here is a list of the top 10 Python machine learning libraries.
1. TensorFlow Python
TensorFlow runs a series of operations on tensors. Tensors are actually an N-dimensional matrix that represents the data. This library runs and trains on Networks that are neutral and later used as AI applications.
TensorFlow is an open-source python library that is used for many numerical computations, python training in Ahmedabad makes it one of the best python libraries if you are searching for a machine learning library.
- Developed by – the Google Brain team of google Google
- Launched in – 2015
- Github – https://github.com/tensorflow/tensorflow
- Written in – Python, CUDA, and C++
Features of TensorFlow
- Helps to visualize minute details of graphs that are harder to get from Numpy or Scikit.
- Offers flexibility and modularity
- Can be trained on CPU and GPU
- Pipeline to train several neural networks and GPUs
- Large community
- Open source
Google voice search and Google Photos are well-known applications that use TensorFlow python
2. NumPy Python
NumPy: NUMerical PYthon is used to process python NumPy arrays. NumPy is powerful when it is dealing with highly complex mathematical functions. It works wonders when it is dealing with multidimensional matrices.
NumPy is known for hanl=dling algebra and Fourier series transformation. TensorFlow uses NumPy at the backend to manipulate tensors
- Developed by – Travis Oliphant
- Launched in – 2005
- Github – https://github.com/numpy/numpy
FEATURES
- It is easy to use and interact
- Makes mathematical calculations and operations simple
- This library has a large community of programmers
- As it manages garbage collection it offers a dynamic structure.
- Helps enhance performance
3. Python SciPy
SciPy: SCIentific PYthon is a machine learning library of python that is open source. With the main focus on scientific computing. The main targeted area of this library is engineering, math, and science. This library has many similarities with a paid tool called MatLab.
SciPy is a rich python machine learning library that is used for linear algebra, Fourier Transforms, specific functions, and many more.
- Developed by – Community library project
- Launched in – 2001
- Github – https://github.com/scipy/scipy
- Written in – Python, C++, C, and Fortran
FEATURES
- Use NumPy array to generate data structures
- Supports numpy. Lib.scimath
- Manages 1-D polynomials in two different systems
- Provides faster computation
4. Python Scikit-Learn
This library is one of the top machine learning libraries of python. The library SciKit- learn provides a huge amount of data to solve heavy complex data. It offers you more than a single metric and a top-notch library that is perfect for machine learning and modelling tools.
- Developed by – David Cournapeau
- Launched in – 2007
- Github – https://github.com/scikit-learn/scikit-learn
- Written in – Python, C++, and Cython
FEATURES
- It observes the effectiveness of supervised models by using various methods
- Stores a massive set of potent algorithms
- Works amazing wheel dealing with texts and images
Spotify and Inria are a few applications that use SciKit-Learn
5. Theano Python
Theaons offers tools that help in designing, executing, and optimizing mathematical models and expressions with multi-dimensional arrays. This library helps in detecting and diagnosing error types. Theano is used in unit-testing and self-verification.
It is one of the most versatile python AI which is used for large-scale computing, even easy to use for specific individual projects.
- Developed by – the Montreal Institute for Learning Algorithms (MILA), University of Montreal
- Launched in – 2007
- Github – https://github.com/Theano/Theano
- Written in – Python, CUDA
Zetaops and Vuclip are some well-known applications that use Theano Python.
6. Keras Python
This neural network API can easily run on top of TensorFlow, Theano, or Cognitive toolkit. Keras is well known for machine learning.
- Developed by – François Chollet
- Launched in – 2015
- Github – https://github.com/keras-team/keras
- Written in – Python
FEATURES
● Works the same way on both CPU and GPU
● Supports all Neural Networks models
● Flexible and easy to utilize
● Suppose several-backend
● Have modular architecture
Uber and Netflix are well-known applications using Keras Python’s machine learning library.
7. Python PyTorch
One of the largest Python libraries for machine learning is Python Pytorch, which provides maximum speed, performance, and flexibility. The biggest contribution of PyTorch in machine learning is to increase the speed of research for escalating machine learning models and making them the least expensive.
- Developed by – Facebook’s AI Research lab
- Launched in – 2016
- Github – https://github.com/pytorch/pytorch
- Written in – Python, CUDA, and C++
FEATURES
● Can be used with other libraries and python machine learning packages
● Provides flexibility
● Optimization in both research and production environment
● Provides robust ecosystem
Apple and Samsung electronics are a few applications using Pytorch
8. Python Pandas
Preparing a dataset is a primary principle activity before training, and Python pandas are known for their extensive data analysis. Pandas provide a high level of tools and data structures
This was developed for extracting and organizing data., and this even offers inbuilt functions and methods to the group, combine and filter the database.
- Developed by – Wes McKinney
- Launched in – 2008
- Github – https://github.com/pandas-dev/pandas
- Written in – Python, Cython, and C
FEATURES
- Amazing tools and data structures for data analysis and manipulations
- Support multiple operations
- Aggregations
- Visualizations
- Concatenations
- Iteration
- Sorting
