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In today’s scenario, Python has become the most powerful programming language among all the languages. There are numerous reasons for it but the most important reason is its huge collection of libraries. Nowadays, many top-notch IT companies prefers to use Python to learn Machine Learning and Artificial Intelligence.
Here in this blog, we will tell you about the top 10 libraries in Python that is used in Machine Learning and Artificial Intelligence by developers.
TensorFlow is one of the famous open source library which was developed by Google in collaboration with the team, Brain. For machine learning, Tensorflow is used in mostly each and every Google Application. If you are writing any program in Python then earlier you have to write at the C++ or CUDA level to run on GPUs. But now through TensorFlow, you can complete or run your written program on your CPU or GPU.
It makes the whole process of acquiring data, training models, serving predictions and refining future results very easy. As it is an open source library, so numerical computation and large-scale machine learning can be done with an ease. It uses Python to learn machine learning as it provides a convenient front-end API for making applications with the framework, during the execution of those applications in high-performance C++.
If you want to work with complex data then Scikit-Learn is one of the best libraries. This library of Python is linked with NumPy and SciPy. this library contains so many number of algorithms which can be implemented in standard machine learning and data mining tasks.
It is quite similarly works like TensorFlow but it is not so productive in comparison to Tensorflow as theano is unable to fit into productions environment.
Pandas is another famous machine learning library in Python. For analysis, pandas gives structure of high-level and ample variety of tools. The biggest aspect of this library is that it can translate complex operations with data only by using one or two commands. This library also has so many inbuilt methods such as grouping, combining data, filtering and time-series functionality as well which make the evaluation of data so easy and quick.
PyTorch is also the biggest Machine Learning library. The developers can perform numerous functions via using it such as tensor computations by acceleration of GPU, calculation of gradients automatically and can create dynamic computational graphs. This library was launched in Python so lately in the year of 2017, still it gains so much of popularity. Due to its success, there is an increase in the number of machine learning developers too.
Keras is one of the trendiest machine learning library in python for the beginners. This is because it allows the easiest mechanism to express neural networks. It also provides some of the top utilities to compile models, graphs visualization, processing data sets and so on. But, in comparison to other machine learning libraries, Keras is little bit slow because to perform operations it makes computational graphs by using backend infrastructure. Apart from that, it also gives so many preprocessed database & pretrained models which includes VGG, Mnist, Inception, SqueezeNet, etc.
For heavy computation, the another wonderful library for machine learning in python is NumPy. If you want to do the numeric computation easily and quickly then it is the best library in python. Like Pandas, NumPy also has so many inbuilt libraries. the knowledge of NumPy arrays is very important as it is the basic and has lots of applications on machine learning, AI and data science based programs.
For scientific and technical computing SciPy library is used as it provides all the tools required for scientific and technical computing.
For plotting, Matplotlib is the perfect option as it provides a flexible plotting and visualisation library. It is the best software which makes Python more knowledgeable in front of its competitors like MatLab and Mathematica. Apart from this, Matplotlib is slightly low-level library as you require to write more codes to attain the advance levels of visualisation. In this package, you can create labels, grids, legends and many more formatting entities. Also, Matplotlib is supported by various platforms and use different GUI kits as well for the depiction of visualisation results.
It is as similar as Matplotlib, as it is also the perfect one for plotting. For plotting common data visualizations, Seaborn is easier than Matplotlib package. Moreover, it offers more pleasant high-level wrapper.
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