Top 10 Python Libraries for Machine Learning : Step-By-Step Process

• In this article you will get
• 1.Introduction to Python for Machine Learning
• 2.Benefits of using Python
• 3.Python Libraries
• 4.Conclusion

Introduction to Python for a Machine Learning

In a 21st century, most applications are somehow built using an Artificial Intelligence, Machine Learning, or Deep Learning, which use a Python Machine Learning library. Usually, AI projects are distinct from the conventional projects in a software industry. Variations in development approaches lie in application framework, the necessary skills needed for an AI-based applications, and need for in-depth analysis.

Benefits of using a Python

1.Simple and compatible: Python provides a descriptive and interactive code. Although the complicated algorithms and adaptable workflows are behind the Artificial Intelligence and Machine Learning, the simplicity of Python Machine Learning library and framework enables application developers to develop a reliable systems.

2.Platform-independent: One aspect adding to success of Python is that it is language that is independent of platform on which it is being operated. Python is supported by a different platforms like Windows, macOS, and Linux. For most commonly used a software, Python language code can be used to build the discrete executable programs.

3.Large community: According to survey done by a Stack Overflow, Python is one of top 10 programming languages used by a different software industries.It is considered a best language for the web development as well.

Python Libraries

The following is list of some of a most commonly used Python libraries:

Pandas:

One of the widely used Machine Learning libraries for a Python is pandas. pandas is a best Python library that is majorly used for data manipulation. It uses handy and descriptive for data structures, like DataFrames, to create programs for an implementing functions. Developed on top of a NumPy, pandas is a quicker and easier-to-use library.

Advantages:

• It has a descriptive, quick, and also compliant data structures.
• It supports the operations such as grouping, integrating, iterating, re-indexing, and representing data.
• It is more flexible for usage in association with the other libraries.
• It can be implemented in large variety of areas, especially related to the business and education, due to its optimized performance.

Disadvantages:

• It is based on a Matplotlib, which means that an inexperienced programmer needs to be acquainted with the both libraries to understand which one will be better to solve a particular business problem.
• It is much less suitable for the quantitative modeling and n-dimensional arrays. In such scenarios, where need to work on a quantitative or statistical modeling, and can use a Numpy or SciPy.

Matplotlib

Matplotlib is the library that is used for a data visualization. Matplotlib is a subpart of a SciPy dealing with NumPy structures and high-level models such as a pandas. Matplotlib is considered as one of important libraries for a machine learning in Python for performing data visualization.

Advantages:

• It helps produce the plots that are configurable, powerful, and accurate.
• It can be simply streamlined with a Jupyter Notebook.
• It supports a GUI toolkits that include the wxPython, Qt, and Tkinter.

Disadvantages:

• It has a strong dependency on a NumPy and other such libraries for a SciPy stack.
• It has a more learning curve as its use takes quite a lot of knowledge and application from a learner’s end.
• It can be confusing for developers as it offer two distinct frameworks, object-oriented and a MATLAB.

Scikit-Learn

Scikit-Learn is the extension of a SciPy. Scikit-Learn is widely used for an implementing Machine Learning algorithms. It then became a widely used library as it is open source and also because to its different features that help to develop a Machine Learning models.

Advantages:

• It has go-to package that consists of all methods for implementing the standard algorithms of a Machine Learning.
• It has a easy and consistent interface that helps fit and transforms a models over any dataset.
• It is most suitable library for the creating pipelines, which helps in building fast prototype.

Disadvantages:

• It is not capable of an employing categorical data in algorithms.
• It is heavily dependent on a SciPy stack.

NumPy

NumPy is regarded as being one of most widely used and best Python libraries for a Machine Learning. Other libraries, like TensorFlow and Keras, use NumPy to implement different operations on tensors.

Advantages:

• It can simple deal with the multidimensional data.
• It helps in matrix manipulation of data and operations like transpose, reshape, and much more.
• It enables the enhanced performance and management of garbage collection by providing the dynamic data structure.

Disadvantages:

• It is highly dependent on a non-Pythonic entities. It uses functionalities of Cython and the other libraries that use C or C++.
• Its more productivity comes at a price.

TensorFlow

Another important Python Machine Learning library is a TensorFlow. It is one of best open-source libraries used for a building Machine Learning and Deep Learning models. Eventually, it gained a lot of the popularity, and it has proved to be resourceful library for more business projects.

Advantages:

• It helps in implementing a reinforcement learning.
• It can straight away visualize Machine Learning models using a TensorBoard.
• The models built using a TensorFlow can be deployed on a CPUs as well as GPUs.

Disadvantages:

• It runs the considerably slower in comparison to CPUs or GPUs that use the other frameworks.
• Its computational graphs are slow when be executed.

Keras

Keras is a widely used a framework or library for fast and efficient experimentation related to the deep neural networks. It is a standalone library comprehensively used for the building Machine Learning or Deep Learning models that help engineers develop applications like Netflix, Uber, and many others.

Advantages:

• It is best library for a research work and efficient prototyping.
• Its framework is be portable.
• It allows simple representation of neural networks.
• It is more efficient for a visualization and modeling.

Disadvantages:

• It is slow as it needs a computational graph before implementing an operation.

Theano

Theano is the library that enables us to assess mathematical operations with help of a multidimensional arrays. It helps engineers to build Deep Learning projects. Theano is high efficient if used on a GPUs rather than working with CPUs.

Advantages:

• It supports the GPUs that help applications perform a complex computations efficiently.
• It is simple to understand and implement because of its integration with a NumPy.
• It has a big community of developers.

Disadvantages:

• It is slower in a backend.
• It has different problems in low-level APIs.
• It gives the lot of backend errors.
• It has steep learning curve.

PyTorch

PyTorch is the framework that enables the execution of a tensor computations. It helps to create an effective computational graphs and provides the extensive API for handling the errors of a neural networks. Pytorch is completely based on open-source framework executed in C, which is called Torch.

Advantages:

• It is famous for its speed of execution.
• It is capable of the handling powerful graphs.
• It helps to integrate with the various Python objects and libraries.

Disadvantages:

• It does not have extensive community; it also lags to offer content for queries.
• It has lesser features in terms of the providing visualizations and application debugging in a comparison to other Python libraries.

SciPy

SciPy is considered one of crucial libraries in a Python. SciPy enables us to perform a scientific computing. SciPy is based on a NumPy and is also a subpart of SciPy stack.

SciPy has different modules for implementing multiple Machine Learning algorithms. The feature that makes a SciPy so important for Machine Learning is that it ensures a quick and high-quality execution. It is also a simple-to-use and quick computing library.

Advantages:

• It is perfect for an image manipulation.
• It provide basic processing features for mathematical operations.
• It offers an effective integration for a numerics and their optimizations.
• It also facilitates a processing of signals.

Disadvantages:

• There is no main disadvantage of using a SciPy. However, there can be confusion between the SciPy stack and SciPy library as the SciPy library is included in a stack.

Seaborn

Seaborn is the library in Python that allows us to create a analytical graphs. Seaborn is based on a Matplotlib and includes the data structures of pandas.

Advantages:

• It produces the graphs that are more appealing than those created with a Matplotlib.
• It has integrated packages that are unavailable in a Matplotlib.
• It uses a less code for visualizing graphs.
• It is integrated with the pandas for the visualizing and analyzing data.

Disadvantages:

• Prior knowledge of a Matplotlib is required to work with a Seaborn.
• Seaborn does not provide a feature of customization, which is there in a Matplotlib.

Conclusion

Come to the end of this article and have discussed a top 10 Python libraries for the Machine Learning including their advantages and disadvantages. have a clear idea about where can use which Python library and what are pros and cons of using them.