In this blog on ‘Top 10 Python Libraries for Machine Learning,’ we will discuss the following:
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Introduction to Python for Machine Learning
In the 21st century, most of the applications developed by companies are somehow built using Artificial Intelligence, Machine Learning, or Deep Learning that uses Python Machine Learning library. Usually, AI projects are distinct from conventional projects in the software industry. Variations in development approaches lie in the application framework, the necessary skills needed for the AI-based application, and the need for in-depth analysis.
Hence, one of the important factors involved in developing AI-based applications is the use of a suitable programming language. We should employ a programming language that is efficient in making the applications stable and extensible. For this, companies use the Python programming language as it offers a lot of libraries and packages for the development task, and hence, it is widely used for working on AI-based projects.
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Benefits of Using Python
Here are a few of the benefits of using Python:
- Simple and compatible: Python provides a descriptive and interactive code. Although complicated algorithms and adaptable workflows are behind Artificial Intelligence and Machine Learning, the simplicity of Python Machine Learning library and framework, enables application developers to develop reliable systems.
- Platform-independent: One aspect adding to the success of Python is that it is a language that is independent of the platform on which it is being operated. There are various platforms that support Python, such as Windows, macOS, and Linux. For the most commonly used software, Python language code can be used to build discrete executable programs. This ensures that Python programs can be quickly deployed, and we can use them without having a Python interpreter on operating systems.
- Large community: According to a survey by Stack Overflow, it is one of the top 10 programming languages used by various software industries. Also, Python is one of the most searched programming languages than any other. It is considered the best language for Web Development as well. It has a large community of developers that can help the newbies starting with Python programming to learn and grow with experienced developers.
Now, as we have discussed Python and its benefits, let’s look at the top 10 Python libraries for Machine Learning.
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One of widely used Python’s Machine Learning library is Pandas. Pandas is the best Python library that is majorly used for data manipulation. It uses handy and descriptive data structures such as DataFrames to create programs for implementing functions. Developed on top of NumPy, it is a quick and easier-to-use library.
Pandas provides data reading and writing capabilities using various sources such as Excel, HDFS, and many more. If you are planning on a use case for a real-world Machine Learning model, then, sooner or later, you would use Pandas for implementing it. Below are the advantages and disadvantages of using Pandas.
- It has descriptive, quick, and compliant data structures.
- It supports operations such as grouping, integrating, iterating, re-indexing, and representing data.
- The Pandas library is very flexible for usage in association with other libraries.
- It contains inherent data manipulation functionalities that can be implemented using minimal commands.
- It can be implemented in a large variety of areas, especially related to business and education, due to its optimized performance.
- It is based on Matplotlib, which means that an inexperienced programmer needs to be acquainted with both libraries to understand which one will be better to solve a specific business problem.
- Pandas is much less suitable for quantitative modeling and n-dimensional arrays. In such scenarios, where we need to work on quantitative/statistical modeling, we can use Numpy or SciPy.
Further, we will look around Matplotlib, which is another Python ML library.
Matplotlib is a library used for data visualization. It is a sub-part of SciPy. It deals with NumPy structures and high-level models such as Pandas. It is considered as one of the essential libraries for machine learning in python for performing data visualization.
To create high-quality plots and charts for visualizations, it provides a plotting environment similar to MATLAB. It also offers a lot of features to make informative visualizations. Now, let’s have a glance at some of the advantages and disadvantages of the Matplotlib library.
- It helps produce plots that are configurable, powerful, and accurate.
- Matplotlib can be easily streamlined with Jupyter Notebook.
- It supports GUI toolkits that include wxPython, Qt, and Tkinter.
- Matplotlib is leveraged with a structure that can support Python as well as IPython shells.
- Matplotlib has a strong dependency on NumPy and other such libraries for the SciPy stack.
- It has a high learning curve as the use of Matplotlib takes quite a lot of knowledge and application from the learners’ end.
- There can be confusion for developers because Matplotlib provides two distinct frameworks: Object-oriented and MATLAB.
- Matplotlib is a library used for data visualization. It is not suitable for data analysis. To get both data visualization and analysis, we will have to integrate it with other libraries.
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The Scikit-Learn library is an extension of SciPy. It is widely used for implementing Machine Learning algorithms. Previously, it was just a part of a summer project at Google. Then, it became a widely used library as it is open-source and also due to its various features that help develop Machine Learning models.
It provides an easy and robust structure that helps the ML models learn, transform, and predict with the help of data. The Scikit-Learn library provides functionalities that help create classification, regression, and clustering models. Also, it offers a wide range of applications for preprocessing, statistical analysis, model assessment, and many more.
- The Scikit-Learn library has a go-to package that consists of all the methods for implementing the standard algorithms of Machine Learning.
- It has a simple and consistent interface that helps fit and transform the model over any dataset.
- It is the most suitable library for creating pipelines that help build a fast prototype.
- It is also the best for the reliable deployment of Machine Learning models.
- Scikit-Learn is not capable of employing categorical data to algorithms.
- It is heavily dependent on the SciPy stack.
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Next, let us look at another Python ML library which is NumPy.
NumPy is regarded as being one of the most widely used and best Python libraries for Machine Learning. Other libraries, such as TensorFlow and Keras, use NumPy to implement various operations on tensors.
Moreover, the NumPy library is very interactive and intuitive and helps us implement complex operations of mathematics in a simple way. Now, let’s look at a few of the advantages and disadvantages of the NumPy library.
- Using NumPy, we can easily deal with multi-dimensional data.
- The library helps in the matrix manipulation of the data and the operations such as transpose, reshape, and many more.
- NumPy enables enhanced performance and the management of garbage collection as it provides a dynamic data structure.
- It allows us to improve the performance of the Machine Learning model.
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- NumPy is highly dependent on non-Pythonic entities. It uses the functionalities of Cython and other libraries that use C/C++.
- Its high productivity comes at a price.
- The data types are hardware-native and not Python-native, so it costs heavily when we want to translate NumPy entities back to Python-equivalent entities and vice versa.
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From the Python Machine Learning library list, we have another most important one that is TensorFlow. It is one of the best open-source libraries used for building Machine Learning and Deep Learning models. It was created by Google’s research team for developing Google products. Eventually, it gained a lot of popularity, and it has proved to be a resourceful library for many business projects by now.
TensorFlow has a powerful ecosystem of tools and resources for the community. Such kinds of toolsets enable engineers to perform research work on Machine Learning and Deep Learning to build efficient applications. Also, Google continues to add a variety of valuable features to TensorFlow to keep up with the pace of the highly competitive world. However, there are some advantages and disadvantages of using Tensorflow, and they are discussed below.
- The TensorFlow library helps us implement reinforcement learning.
- We can straight away visualize Machine Learning models using TensorBoard, a tool in the TensorFlow library.
- We can deploy the models built using TensorFlow on CPUs as well as GPUs.
- It runs considerably slower in comparison to those CPUs/GPUs that are using other frameworks.
- The computational graphs in TensorFlow are slow when executed.
Keras is a widely used framework/library for fast and efficient experimentation related to deep neural networks. It is a standalone library comprehensively used for building ML/DL models that help engineers develop applications, such as Netflix, Uber, and many more.
It is a user-friendly library designed to reduce the difficulty of developers in creating ML-based applications. Further, it provides multi-backend support that helps developers integrate models with a backend for providing the application with high stability. Let’s now check out the advantages and disadvantages of using Keras.
- Keras is the best for research work and efficient prototyping.
- The Keras framework is portable.
- It allows an easy representation of neural networks.
- It is highly efficient for visualization and modeling.
- Keras is slow as it requires a computational graph before implementing an operation.
Theano is a library that enables us to assess mathematical operations with the help of multi-dimensional arrays. It helps engineers build Deep Learning projects. Theano is more efficient if used on GPUs rather than working with CPUs.
It is best used to establish, optimize, and assess mathematical expressions, employing a multi-dimensional array. Moreover, it is used on models to diagnose errors by performing unit testing with self-verification. Below are the pros and cons of using Theano.
- Theano supports GPUs that help applications perform complex computations efficiently.
- It is easy to understa implement Theano because of its integration with NumPy.
- There is a huge community of developers using Theano.
- Theano is slower in the backend.
- There are various problems in Theano’s low-level API.
- It gives a lot of backend errors.
- Also, the Theano library has a steep learning curve.
PyTorch is a framework that enables the execution of tensor computations. It helps create effective computational graphs and provides an extensive API for handling the errors of neural networks. The Pytorch library is completely based on an open-source framework executed in C programming language, which is called Torch.
There are various features that make PyTorch popular, such as hybrid frontend and distributed training, including multiple tools that help build efficient systems. Also, it is used to create systems leveraged with natural language processing. Now, let’s have a glance at the advantages and disadvantages of using PyTorch.
- The PyTorch framework is popular for its speed of execution.
- It is capable of handling powerful graphs.
- It also helps integrate with various Python objects and libraries.
- The community for PyTorch is not extensive, and it lags to provide content for queries.
- In comparison to other Python frameworks, PyTorch has lesser features in terms of providing visualizations and application debugging.
SciPy is considered as one of the crucial libraries in Python that enables us to perform scientific computing. The SciPy library is based on NumPy, and it is also a sub-part of the SciPy stack.
It has various modules for implementing multiple Machine Learning algorithms. The feature that makes it so important for Machine Learning is that it ensures a quick and high-quality execution. Also, it is a simple-to-use and fast computing library.
- SciPy is perfect for image manipulation.
- It offers basic processing features for mathematical operations.
- It provides effective integration for numerics and their optimizations.
- It also facilitates the processing of signals.
- There is no major disadvantage of using SciPy. However, there can be confusion between the stack and SciPy as the SciPy library is included in the stack.
Seaborn is a framework/library in Python that allows us to create analytical graphs. The Seaborn library is based on Matplotlib and includes data structures of Pandas.
A few of the advantages and disadvantages of the Seaborn library in Python are discussed below.
- Seaborn produces graphs that are more appealing than those created with Matplotlib.
- It has integrated packages that are unavailable in Matplotlib.
- For visualizing graphs, the Seaborn library uses less code.
- It is integrated with Pandas for visualizing and analyzing data.
- We should have prior knowledge of Matplotlib to work with Seaborn.
- Seaborn does not provide the feature of customization, which is there in Matplotlib.
Finally, we have come to the end of this blog and have discussed all the top 10 Python libraries for Machine Learning, including their advantages and disadvantages. I hope by now, you have a clear idea about where you can use all Python libraries for Machine Learning and what the pros and cons will be of using them.
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