# Best Python Libraries For Machine Learning

As you know python has a treasure of amazing and very powerful python libraries for Machine Learning, Artificial Intelligence, Data Science, and other specific tasks. According to data, There are over 137,000 python libraries and 198,826 python packages ready to ease developer’s regular programming experience. Here I’ll introduce the 7 Best Python Libraries For Machine Learning.

Before moving forward let me introduce what is machine learnig.

**Machine Learning**: Machine learning is a field of study that provides machines the ability to automatically learn and improve from experience without being explicitly programmed.

So, let’s start 😉

### Amazing Python Libraries

### 1. TensorFlow

When we talk about machine learning, Tensorflow is always comes in our mind. TensorFlow is an end-to-end open-source platform for machine learning.

Tensorflow 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.

**Features of TensorFlow**

- Responsive Construct
- It’s Flexible
- Easily Trainable
- Parallel Neural Network Training
- Large Community
- It’s Free for all (Open Source)
- Availability of Statistical Distributions

## 2. Numpy

NumPy is an essential library for scientific computing. It is an opensource library under a liberal BSD license.

NumPy offers mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms and plays well with distributed, GPU, and sparse array libraries. This is why Numpy is in the best Python libraries for machine learning.

### Features Of NumPy

- A powerful N-dimensional array object.
- Sophisticated (broadcasting) functions.
- Tools for integrating C/C++ and Fortran code.
- Useful linear algebra, Fourier transform, and random number capabilities.

## 3. PyTorch

PyTorch is an open-source machine learning library for Python, based on Torch. It is used for applications such as computer vision and natural language processing.

It was developed by Facebook’s artificial-intelligence research group, and Uber’s “Pyro” Probabilistic programming language software is built on it. PyTorch is free and open-source software released under the Modified BSD license.

### Features Of PyTorch

- Distributed Training
- Robust Ecosystem
- Native ONNX Support
- C++ Front-end
- Cloud Support

# 4. SciPy

The SciPy library is one of the core packages that make up the SciPy stack. It is fundamental library of scientific computing like NumPy.

Scipy provides many user-friendly and efficient numerical routines, such as routines for numerical integration, interpolation, optimization, linear algebra, and statistics. This can be one reason why scipy is in the best Python libraries for machine learning list.

SciPy is a community-driven project and development happens on GitHub. It is fiscally sponsored by NumFOCUS.

### Features Of SciPy

- SciPy contains modules for optimization
- Linear algebra, integration, interpolation and special functions
- Image processing and ODE solvers
- Some common in science and engineering

## 5. Scikit-Learn

Scikit-learn is one of the most useful libraries for machine learning in Python. It’s also known as sklearn.

The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, Model selection, Preprocessing, and dimensionality reduction. This is why Scikit-Learn is in the best Python libraries for machine learning.

### Features Of Scikit-Learn

- Simple and efficient tools for predictive data analysis
- Accessible to everybody, and reusable in various contexts
- Built on NumPy and SciPy
- Open source, commercially usable – BSD license

## 6. Theano

Another library which is very popular for machine learning and as well as in deep learning. Theano is a Python library that lets you define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).

Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data. It can also surpass C on a CPU by many orders of magnitude by taking advantage of recent GPUs.

### Features Of Theano

- Tight integration with NumPy
- Transparent use of a GPU
- Efficient symbolic differentiation
- Speed and stability optimizations
- Dynamic C code generation
- Extensive unit-testing and self-verification

## 7. Keras

Keras is an open-source neural network library written in Python. (can also be used for machine learning)

It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

### Features Of Keras

- Keras is a high-level interface and uses Tensorflow for its backend.
- Keras supports almost all the models of a neural network – fully connected, convolutional, pooling, recurrent, embedding, etc. Furthermore, these models can be combined to build more complex models.
- Keras, being modular in nature, is incredibly expressive, flexible, and apt for innovative research.
- It can runs smoothly on both CPU and GPU.
- Keras is a completely Python-based framework.

### 8. Matplotlib

It is a comprehensive library for creating static, animated, and interactive visualizations in Python. we can plot 2D or 3D Graphs. Often mathematical or scientific applications require more than single axes in a representation. With this library, we can build multiple plots at a time.

**Features of Matplotlib**

- It has the ability to play well with many operating systems
- It supports dozens of backends and output types
- We can create great quality figures that are really good for a publication
- You can use MatPlotlib with different toolkits such as Python Scripts, IPython Shells, Jupyter Notebook, and many other four graphical user interfaces

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