Top 9 Python Libraries for Machine Learning

Python Libraries for Machine Learning, as the name suggests, is type of programing where program a computer by which they are able to learn from different kinds of data. A more about general definition in detail given by Arthur Samuel is – “Machine Learning is the field of study that gives computers the ability to learn without being distinctly programmed.” They actually are used to solve many kinds of real life problems.

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Conventional software projects are different from AI and ML projects as always and also it varies based on the technologies, skills for ML-based projects and the demand for in-depth research. You have to choose a programming language for building ML and AI outline, which one should be flexible, stable and include predefined libraries & frameworks. In today’s time python is one of the programming language in which you can see many Python machine learning and Artificial Intelligence projects developing.

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Python machine learning libraries have grown and more popular now days and its preferred language for machine learning algorithm implementations. Let’s start and look at the Python libraries used for machine learning.

Python Libraries for Machine Learning

  • Numpy
  • Scipy
  • Scikit-learn
  • Theano
  • TensorFlow
  • Keras
  • PyTorch
  • Pandas
  • Matplotlib

1. NumPy (Python Libraries for Machine Learning)

NumPy is a well-known general-purpose array-processing package. A vast collection of high complex mathematical functions which makes NumPy much powerful to process big multi-dimensional arrays and matrices. Other libraries like Tensorflow also use NumPy at the backend for manipulating tensors.

NumPy can also act as an efficient multi-dimensional container for any generic data that is in any datatype. Powerful N-dimensional array object, broadcasting functions, and out-of-box tools are key features of NumPy to integrate C/C++ and Fortran code

2. SciPy

SciPy is a grown and very popular library among Machine Learning as it contains different modules for optimization, linear algebra, integration and statistics. Yes also there is a difference between the SciPy library and the SciPy stack. The SciPy is one of the core packages that make up the SciPy stack and also very useful for image manipulation.

The SciPy library offers modules for linear algebra, image optimization, integration interpolation, special functions, Fast Fourier transform, signal and image processing, Ordinary Differential Equation (ODE) solving, and other computational tasks in science and analytics. SciPy uses underlying data structure which multi-dimensional array is provided by the NumPy module. For array manipulation subroutines SciPy depends on NumPy. The SciPy library was developed to work with NumPy arrays along with providing numerical and efficient user-friendly functions.

3. Scikit-learn

Python Libraries for Machine Learning

Scikit-learn is open-source Python machine learning library along with a broad range of clustering, regression and classification algorithms. It can operate with numeric and scientific libraries of Python like example NumPy and SciPy. It is a commercially usable artificial intelligence library. Scikit-learn can supports both supervised as well as unsupervised ML. Below is the list of the primary benefits of Scikit-learn Python that makes it the most preferable Python libraries for machine learning.

  • Reduction of dimensionality
  • Decision tree pruning & induction
  • Decision boundary learning
  • Feature analysis & selection
  • Outlier detection & rejection
  • Advanced probability modeling
  • Unsupervised classification & clustering

4. Theano

Theano is a python machine learning library that can act as an optimizing compiler for evaluating and manipulating mathematical expressions and matrix calculations. Built on NumPy, Theano exhibits a tight integration with NumPy and has a very similar interface. Theano can work on Graphics Processing Unit (GPU) and CPU.

It is achieved by optimizing the utilization of CPU and GPU. It is also broadly used for unit-testing and self-verification to detect and diagnose different kinds of errors. Theano has been used in large-scale computationally intensive scientific projects for a long time and but it’s very simple approachable enough to be used by individuals for their own projects and also a very powerful library.

5. TensorFlow

TensorFlow is developed by Google in collaboration with Brain Team and it is an open-source Python library. TensorFlow is used for writing new and powerful algorithms that include a large number of tensor operations. Through computational graph, neural networks can be easily expressed; they can be implemented as a series of operations on Tensors using TensorFlow. Apart from this, these tensors are N-dimensional matrices also that represent your data.

Unlike Numpy and other libraries, TensorFlow lets you easily visualize each and every part of the graph which is not an option while using Numpy or SciKit. For distributed computing it’s easily trainable on CPU as well as GPU. TensorFlow is used in some of Google application for machine learning.

6. Keras

Keras has over 200,000 users as of November 2017.It is used for neural networks and machine learning and Keras is also open source. Keras also can run on top of Theano, TensorFlow, R, or PlaidML Microsoft Cognitive Toolkit,. Keras also can run easily and efficiently on CPU and GPU. 

It offers almost all standalone modules which is able to function independently including optimizers, activation functions, neural layers, cost functions, initialization schemes, and regularization schemes. It makes it easy to add new modules just like adding new functions and classes. As the model is already defined in the code, so there is no need to have a separate model config files. If you are beginners because Keras makes it simple for machine learning beginners to design and develop a neural network. Keras Python also deals with convolution neural networks. It includes algorithms for normalization, optimizer, and activation layers. Instead of being an end-to-end Python machine learning library, Keras functions as a user-friendly, extensible interface that enhances modularity & total expressiveness.

7. PyTorch

PyTorch is most popular among the popular Python libraries for machine learning in such a short time and why it is most popular?

PyTorch is a production-ready Python machine-learning library with excellent examples, strong community supports applications and use cases. This library absorbs strong GPU acceleration and enables you to apply it from applications like NLP.

 As it supports GPU and CPU computations, it provides you with performance optimization and scalable distributed training in research as well as production. Deep neural networks and Tensor computation with GPU acceleration are the two high-end features of the PyTorch. It includes a machine learning compiler called Glow that boosts the performance of deep learning frameworks.

8. Pandas

In machine learning projects, a substantial amount of time is spent on preparing the data as well as analyzing basic trends & patterns.

Below are two main kinds of data structures used by pandas are :

  • Series (1-dimensional)
  • DataFrame (2-dimensional)

Both data structures put together can handle extensive majority of data requirements and use cases from most sectors like science, statistics, social, finance, and of course, analytics and also other areas of engineering. 

Pandas support and play well role with different types of data including the below:

  • Tabular data with columns of heterogeneous data. For instance, consider the data coming or retrieving from the SQL table or any Excel spreadsheet.
  • Ordered and unordered time series data. Pandas is exceptionally robust in handling uneven time-series data
  • Arbitrary matrix data with the homogeneous or heterogeneous type of data in the rows and columns
  • Any other form of statistical or observational data sets. The data need not be labeled at all.

9. Matplotlib

Matplotlib is a data visualization library that is used for 2D plotting to produce publication-quality image plots and figures in a variety of formats. The library helps to generate histograms, plots, error charts, scatter plots, bar charts with just a few lines of code.

It is a 2D plotting library used for creating 2D graphs and plots. There is module which is called pyplot makes it very easy for programmers for plotting as it provides the features which controls line styles, font properties, formatting axes, etc. It provides various kinds of graphs and plots for data visualization, viz., histogram, error charts, bar chats, etc,

Conclusion

That’s it for this article about Python Libraries for Machine Learning, If you know your requirements clearly, I don’t think you will face any problem in picking up the right python library for machine learning. If you know any other useful library that should be present in this list, do let us know in the comments below.

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