This blog covers some of the most popular and useful Python libraries for machine learning. It discusses libraries like Scikit-Learn for general ML tasks, TensorFlow and PyTorch for deep learning, Pandas for data analysis, NumPy for numerical computing, and Matplotlib for visualization.
Table of Content
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Introduction to Python for Machine Learning
In the 21st century, most applications are somehow built using Artificial Intelligence, Machine Learning, or Deep Learning, which use the 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 AI-based applications, and the need for in-depth analysis.
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 Python, as it offers a lot of libraries and packages for development tasks. Hence, Python is widely used for working on AI-based projects.
Benefits of using Python
Here are a few of the benefits of using Python:
- Simple and compatible: Python provides descriptive and interactive code. Although complicated algorithms and adaptable workflows are behind Artificial Intelligence and Machine Learning, the simplicity of the 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. Python is supported by various platforms 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 done by Stack Overflow, Python is one of the top 10 programming languages used by various software industries. Python is also one of the most searched programming languages. It is considered the best language for web development as well. It has a large community of developers that can help newbies starting with Python programming to learn and grow with experienced developers.
Now that we have discussed Python and its benefits, let us take a look at the top 10 Python libraries for Machine Learning.
Python Libraries
The following is the list of some of the most commonly used Python libraries:
pandas
One of the widely used Machine Learning libraries for Python 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, pandas is a quicker and easier-to-use library.
pandas provide data reading and writing capabilities using various sources such as Excel, HDFS, and others. If you are planning on a use case for a real-world Machine Learning model, then, sooner or later, you will use pandas for implementing the same. Below are the advantages and disadvantages of using pandas.
Advantages:
- It has descriptive, quick, and compliant data structures.
- It supports operations such as grouping, integrating, iterating, re-indexing, and representing data.
- It 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.
Disadvantages:
- 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.
- It is much less suitable for quantitative modeling and n-dimensional arrays. In such scenarios, where we need to work on quantitative or statistical modeling, we can use Numpy or SciPy.
Next, we will take a look at Matplotlib, which is another Python Machine Learning library.
Matplotlib
Matplotlib is a library that is used for data visualization. Matplotlib is a subpart of SciPy dealing with NumPy structures and high-level models such as pandas. Matplotlib 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, Matplotlib provides a plotting environment similar to MATLAB. Matplotlib also offers a lot of features to make informative visualizations.
Below are some of the advantages and disadvantages of Matplotlib.
Advantages:
- It helps produce plots that are configurable, powerful, and accurate.
- It can be easily streamlined with the Jupyter Notebook.
- It supports GUI toolkits that include wxPython, Qt, and Tkinter.
- It is leveraged with a structure that can support Python as well as IPython shells.
Disadvantages:
- It has a strong dependency on NumPy and other such libraries for the SciPy stack.
- It has a high learning curve as its use takes quite a lot of knowledge and application from the learner’s end.
- It can be confusing for developers as it provides two distinct frameworks, object-oriented and MATLAB.
- It is primarily used for data visualization. It is not suitable for data analysis. To get both data visualization and data analysis, we will have to integrate it with other libraries.
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Scikit-Learn
Scikit-Learn is an extension of SciPy. Scikit-Learn is widely used for implementing Machine Learning algorithms. Previously, it was just a part of a summer project at Google. It then became a widely used library as it is open source and also because to its various features that help develop Machine Learning models.
Scikit-Learn provides an easy and robust structure that helps Machine Learning models learn, transform, and predict with the help of data. Scikit-Learn provides functionalities that help create classification, regression, and clustering models. It also offers a wide range of applications for preprocessing, statistical analysis, model assessment, and much more.
Advantages:
- It 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 transforms the models over any dataset.
- It is the most suitable library for creating pipelines, which helps in building a fast prototype.
- It is the best library for the reliable deployment of Machine Learning models.
Disadvantages:
- It is not capable of employing categorical data in algorithms.
- It is heavily dependent on the SciPy stack.
Next, we will take a look at another Python Machine Learning library, NumPy.
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, NumPy is very interactive and intuitive and helps in the implementation of complex mathematical operations in a simple way. Below are some advantages and disadvantages of NumPy.
Advantages:
- It can easily deal with multidimensional data.
- It helps in the matrix manipulation of data and operations such as transpose, reshape, and much more.
- It enables enhanced performance and management of garbage collection by providing a dynamic data structure.
- It allows improving the performance of Machine Learning models.
Disadvantages:
- It is highly dependent on non-Pythonic entities. It uses the functionalities of Cython and other libraries that use C or C++.
- Its high productivity comes at a price.
- Its data types are hardware-native and not Python-native, so it costs heavily when NumPy entities have to be translated back to Python-equivalent entities and vice versa.
TensorFlow
Another important Python Machine Learning library 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.
TensorFlow in Python 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. Google also continues to add a variety of valuable features to TensorFlow to keep up with the pace of the highly competitive world. Below are some advantages and disadvantages of using Tensorflow.
Advantages:
- It helps in implementing reinforcement learning.
- It can straight away visualize Machine Learning models using TensorBoard.
- The models built using TensorFlow can be deployed on CPUs as well as GPUs.
Disadvantages:
- It runs considerably slower in comparison to the CPUs or GPUs that use other frameworks.
- Its computational graphs are slow when executed.
Keras
Keras is a widely used framework or library for fast and efficient experimentation related to deep neural networks. It is a standalone library comprehensively used for building Machine Learning or Deep Learning models that help engineers develop applications such as Netflix, Uber, and many others.
Keras is a user-friendly library designed to reduce the difficulty of developers in creating ML-based applications. It also provides multi-backend support that helps developers integrate models with the backend for providing high stability to the application. Below are some advantages and disadvantages of using Keras.
Advantages:
- It is the best library for research work and efficient prototyping.
- Its framework is portable.
- It allows easy representation of neural networks.
- It is highly efficient for visualization and modeling.
Disadvantages:
- It is slow as it requires a computational graph before implementing an operation.
Theano
Theano is a library that enables us to assess mathematical operations with the help of multidimensional arrays. It helps engineers build Deep Learning projects. Theano is more efficient if used on GPUs rather than working with CPUs.
Theano is best used to establish, optimize, and assess mathematical expressions. Moreover, it is used on models to diagnose errors by performing unit testing with self-verification. Below are the advantages and disadvantages of using Theano.
Advantages:
- It supports GPUs that help applications perform complex computations efficiently.
- It is easy to understand and implement because of its integration with NumPy.
- It has a huge community of developers.
Disadvantages:
- It is slower in the backend.
- It has various problems in low-level APIs.
- It gives a lot of backend errors.
- It has a steep learning curve.
PyTorch
PyTorch is a framework that enables the execution of tensor computations. It helps to create effective computational graphs and provides an extensive API for handling the errors of neural networks. Pytorch is completely based on an open-source framework executed in C, which is called Torch.
There are various features that make PyTorch popular, such as hybrid frontend and distributed training, that help build efficient systems. It is also used to create systems leveraged with natural language processing. Below are some of the advantages and disadvantages of using PyTorch.
Advantages:
- It is popular for its speed of execution.
- It is capable of handling powerful graphs.
- It helps to integrate with various Python objects and libraries.
Disadvantages:
- It does not have an extensive community; it also lags to provide content for queries.
- It has lesser features in terms of providing visualizations and application debugging in comparison to other Python libraries.
SciPy
SciPy is considered one of the crucial libraries in Python. SciPy enables us to perform scientific computing. SciPy is based on NumPy and is also a subpart of the SciPy stack.
SciPy has various modules for implementing multiple Machine Learning algorithms. The feature that makes SciPy so important for Machine Learning is that it ensures quick and high-quality execution. It is also a simple-to-use and fast computing library.
Advantages:
- It 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.
Disadvantages:
- There is no major disadvantage of using SciPy. However, there can be confusion between SciPy stack and SciPy library as the SciPy library is included in the stack.
Seaborn
Seaborn is a library in Python that allows us to create analytical graphs. Seaborn is based on Matplotlib and includes the data structures of pandas.
Below are some advantages and disadvantages of Seaborn.
Advantages:
- It produces graphs that are more appealing than those created with Matplotlib.
- It has integrated packages that are unavailable in Matplotlib.
- It uses less code for visualizing graphs.
- It is integrated with pandas for visualizing and analyzing data.
Disadvantages:
- Prior knowledge of Matplotlib is required to work with Seaborn.
- Seaborn does not provide the feature of customization, which is there in Matplotlib.
Conclusion
Finally, we have come to the end of this blog and have discussed the top 10 Python libraries for Machine Learning including their advantages and disadvantages. We hope that by now, you have a clear idea about where you can use which Python library and what are the pros and cons of using them.