Best Python Libraries for Machine Learning


Best Python Libraries for Machine Learning

Python is often the language of choice for developers who need to implement  data analysis or statistical techniques in their business or any work.  It is also used by data scientists whose tasks need to be integrated with a web application or production environment.

The combination of consistent syntax, low development time and flexibility makes python language well suited for development of complex models and prediction engines.

One of the biggest benefit of Python is it possess large number of libraries.

Libraries are set of routines and functions that are written in a given language. A strong set of libraries makes it easier for developers to do complex tasks without rewriting many lines of code.

Some of the most used libraries in machine learning are as follows:

1. Scikit-learn

Best Python Libraries for Machine Learning

Scikit-Learn is one of the most popular and widely used Machine Learning library. It supports many supervised and unsupervised learning algorithms.  Some of the example includes decision tree, linear and logistic regressions, instrument, clustering, and so on.

Scikit-learn is made on two basic libraries of Python, Numpy, and Scipy.  It adds a set of algorithms for common machine learning and data mining tasks, including clustering, regression and classification. Even tasks such as data transferring, feature selection and ensemble methods can be implemented in some lines.

For a beginner in Machine Learning, Scikit-Learn is one of the most adequate tools to work with, until you start implementing complex algorithms.

2. TensorFlow

Best Python Libraries for Machine Learning

If you are in the world of Machine Learning, then you have likely heard about, tried or implemented some kind of deep learning algorithms. Are they necessary? Not always. Are they cool when they are right?  Yes, they are pretty cool.

The interesting thing about
Tensorflow is that when you write a program in Python, you can compile and run on your CPU or GPU.  So you don’t have to write at the C++ or CUDA level to run on GPU.

It uses a system of multi-layered junction that allows you to quickly install, train and deploy the artificial neural network with large datasets.  This is what Google allows to recognize objects in photos or understand words spoken in their voice-recognition app.

3. Theano

Best Python Libraries for Machine Learning

Theano is another good Python library for numerical calculations, and is almost same as Numpy. Theano allows you to efficiently define, optimize and evaluate mathematical expressions including multi-dimensional arrays.

4. Matplotlib


Matplotlib is a standard Python library used by all the data scientist for creating Graphs and 2D plots. It requires more commands to generate nice-looking graphs and figures in comparison with some advanced libraries.

However, the second aspect of this library is its flexibility. With enough commands, you can create any type of graph that you want. You can create different charts, from histograms and scatterplots to non-Cartesian coordinates graph.

It supports various GUI backends on all operating systems, and can export graphics to normal vector and graphic formats like SVG, PDF, JPG, BMP, PNG, GIF, etc.

5. Seaborn

Best Python Libraries for Machine Learning

Seaborn is a popular visualization library that builds on the foundation of Matplotlib. It is a high-level library and it  is more efficient than Matplotlib. It is useful in generating different types of plots including time series, heat maps, and violin plots.

Other most essential python libraries includes Keras, Pandas, PyTorch, Plotly, Bokeh, etc.
Anything Missing? If yes then please comment it in the comment section. Don't feel shy. Free feel to share your reviews.

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