Are you a machine learning geek? Do you like to build and train deep learning models to produce exemplary applications? If your answer to these questions is a **Yes **then make sure to stick around to know about some of the best packages/libraries Python offers to perform conventional machine learning and specialized deep learning. All the libraries which will be discussed in this article provide great support and ease to do machine learning as they are highly optimized and handle all the mathematical hard work solely by themselves.

The following are some of the most popular and in-demand Python libraries which are widely used today to perform machine learning.

## Scikit-learn

Scikit-learn is an open-source Python library that is built on top of NumPy, SciPy, and Matplotlib. It is one of the most popular machine learning libraries and is widely used among researchers and practitioners for predictive analysis. There are several interesting features that Scikit-learn offers including classification, regression, clustering, dimensionality reduction, model selection and preprocessing.

Scikit-learn offers a number of algorithms for supervised, semi-supervised and unsupervised learning. You can view an exhaustive list of all algorithms here. It also contains a bunch of neural network models to carry out supervised deep learning.

You can install Scikit-learn using the most popular package management system for Python known as **pip**. Run the following command for installation.

`pip install scikit-learn`

If you like the **Anaconda** environment more, run the following command to install Scikit-learn.

`conda install scikit-learn`

## Statsmodels

Statsmodels is another open-source Python package that assists in performing machine learning tasks. It is specifically designed to perform statistical machine learning and consists of various functionalities to execute statistical modeling, training, testing and statistical data exploration. If you are looking for algorithms that execute regression using linear models then Statsmodels might be an ideal choice for you. Statsmodels is also extremely famous for time series analysis and forecasting.

You can install Statsmodels using the most popular package management system for Python known as **pip**. Run the following command for installation.

`pip install statsmodels`

If you like the **Anaconda** environment more, run the following command to install Statsmodels.

`conda install -c conda-forge statsmodels`

## TensorFlow

TensorFlow is an open-source Python library by Google specifically designed in favor of deep learning. Deep learning is a branch of machine learning which is loosely inspired by how the human brain works. Earlier, TensorFlow solved the tasks that required heavy numerical computations and hence the library developers soon geared it towards machine learning and deep neural networks. You might have heard that TensorFlow is very fast, that is true! Due to a C/C++ backend, TensorFlow enables itself to run faster than a Python code.

Every TensorFlow application uses a data structure called a **data flow graph **which helps a great deal in performing forward, as well as, backward propagation. TensorFlow offers several advantages for an application including faster compilation time, support for CPUs, GPUs and distributed computing in a cluster.

You can install TensorFlow using the most popular package management system for Python known as **pip**. Run the following command for installation.

`pip install tensorflow`

If you like the **Anaconda** environment more, run the following command to install TensorFlow.

`conda install tensorflow`

## Keras

Keras is another open-source library in Python built especially for neural networks and deep learning. It is a wrapper on top of either TensorFlow, Theano or CNTK. One of the most amazing advantages of Keras is that it enables fast and easy prototyping and hence it is commonly used in areas that require easy and fast experimentation. Just like TensorFlow, it also supports both CPUs and GPUs to perform computations.

Keras supports two ways of model composition; sequential and functional. A sequential model is a linear stack of layers connected in such a manner that every layer is connected to the layers which are one step above and below in the stack. We will learn about sequential and functional composition models in much detail in future articles.

You can install Keras using the most popular package management system for Python known as **pip**. Run the following command for installation.

`pip install Keras`

If you like the **Anaconda** environment more, run the following command to install Keras.

`conda install -c conda-forge keras`

## PyTorch

PyTorch is an open-source scientific computation Python package. Developers actively use it today for deep learning purposes. The key highlighting feature which distinguishes PyTorch from other deep learning libraries is the support for dynamic computation graphs. Dynamic computation graphs ensure that the graph will be available for alteration even at run time. At every point of code execution, we can actually build the graph again and manipulate it at run time based on the needs.

PyTorch enables the support for CUDA which ensures that the code can run on GPU, hence contributing to the fast-paced model training and execution.

You can install PyTorch using the most popular package management system for Python known as **pip**. Run the following command for installation.

`pip install torch==1.4.0+cpu torchvision==0.5.0+cpu -f https://download.pytorch.org/whl/torch_stable.html`

If you like the **Anaconda** environment more, run the following command to install PyTorch.

`conda install pytorch torchvision cpuonly -c pytorch`

Above mentioned are some of the popular and in-demand Python packages for machine learning and deep learning. Each library comes with a different feature set and offers a lot of functionalities to effectively perform data loading, training and testing machine learning and deep learning models. We encourage readers to explore these libraries and select the most suitable library according to their use case and application.

If you wish to learn more about Python, you can check out our collection of Python tutorials.

What’s your favorite Python package for machine learning? Let us know in the comments below.

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