Top 12 Python Libraries for Machine Learning Tasks in 2020
Machine learning is the science of programming computers by which they are able to learn from data. Machine learning gives computers the ability to learn without being programmed explicitly. It is through machine learning that computers are able to perform tasks without receiving explicit instructions to do so.
Traditionally, individuals had to code all the algorithms, mathematical and statistical formulas manually in order to perform machine learning tasks. This was a time consuming, inefficient, and tedious process.
Today, it is very easy and efficient to perform machine learning tasks. This has been made possible by various libraries, frameworks and modules.
Python is the most popular computer programming language for performing machine learning tasks. This can be attributed to a number of factors including the availability of machine learning libraries for Python and the simple Python syntax.
In this article I will be discussing the top Python libraries used to perform machine learning tasks.
Top Python Libraries for Machine Learning
The following are the top Python libraries used in machine learning:
Numpy is a popular Python library used for processing large multi-dimensional arrays and matrices.
It comes with a set of high-level mathematical functions. It helps machine learning programmers to perform fundamental scientific computations. It is very useful for doing Fourier Transform, linear algebra, and random number tasks.
Numpy offers a speedy computation and execution of complicated functions working on arrays. With NumPy, programmers can create arbitrary data types. It also integrates well with databases.
Other features offered by Numpy include:
- Selection and sorting capabilities.
- Shape manipulation.
- Random simulations.
- Statistical operations.
- Logical operations.
TensorFlow is a scalable, fast, and flexible machine learning library. It is open-source and is commonly used for production and research. It is among the most popular libraries for doing machine learning tasks in Python.
TensorFlow is offered by Google, and it makes it easy for both beginners and experts to make machine learning models.
With TensorFlow, programmers can develop and train machine learning models on computers, servers, and portable/mobile devices.
This is made possible by the availability of TensorFlow Serving and TensorFlow Lite that offer similar benefits but for high-performance servers and mobile devices respectively.
The common machine learning tasks that can be accomplished using TensorFlow include:
- Natural language processing.
- Deep neural networks.
- Partial differential equations (PDEs).
- Text, speech, and image recognition.
- Abstract capabilities.
- Effortless collaboration of ideas and code.
The Keras library is a popular Python library for the creation and training of neural network models. It is supported by TensorFlow, hence, you can use it with TensorFlow.
The Keras library comes with tools and building blocks for creating neural networks. These include:
It extends TensorFlow’s functionality with the above features for machine learning programming.
It also has a helpful user community, making it easy for you to get support. Its Slack Channel is also highly dedicated.
It also supports recurrent and convolutional neural networks as well as the standard networks.
PyTorch is a Python machine learning library created by Facebook.
Other than Python, it also supports C++, with the C++ interface. The library offers direct competition to TensorFlow.
The features that differentiate PyTorch from TensorFlow include:
- It’s easy to use, learn, and integrate with the entire Python ecosystem.
- Tensor computing and the ability to support accelerated processing using Graphics Processing units (GPUs).
- Supports neural networks built on auto diff systems (tape-based).
PyTorch is a highly customizable library, hence, it is highly used for deep learning research.
It’s a common machine learning library for Python.
Scikit-learn can be easily integrated with other machine learning libraries such as Pandas and NumPy.
The greatest advantage of Scikit-learn is that it supports a wide variety of machine learning algorithms including the following:
Scikit-learn was built to be flexible and easy for use. All algorithms supported by Scikit-learn work on a consistent interface in Python.
Scikit-learn is also a popular library for performing data mining and data analysis tasks. The library does data modeling instead of tasks like loading, manipulation, handling, and data visualization.
It’s a complete machine learning library, right from research to deployment.
Pandas is a Python library for data analysis and is primarily used for data analysis and data manipulation. It is very usable before the preparation of the dataset for training.
With Pandas, machine learning programmers find it easy to work with structured and time series multidimensional data. When doing machine learning tasks, programmers are able to load their data from files and databases into Pandas data structures.
The features offered by Pandas for handling data include:
- Dataset pivoting and reshaping.
- Joining and merging datasets.
- Data alignment and handling missing data.
- Indexing options like Fancy indexing and Hierarchical axis indexing.
- Data filtration.
With Pandas, you get access to different types of data structures that you can use to organize and store your data. Examples of the Pandas data structures include the Pandas DataFrame and the Pandas Series.
The DataFrame is a common data structure among Pandas users, and it’s just a representation of data in 2 dimensions. The major use of Pandas is data analysis and manipulation.
The Natural Language Processing Toolkit (NLTK) is a Python library for natural language processing. NLTK is a popular library for processing human language.
NLTK comes with a simple interface and a wide variety of lexical resources like WordNet, Word2Vec, FrameNet, and many others.
The common uses of NLTK include:
- Searching for keywords from documents.
- Classification and tokenization of texts.
- Handwriting and voice recognition.
- Word Lemmatization and stemming.
NLTK is considered to be suitable for engineers, students, linguists, industries, and researchers dealing with language.
MLlib is a machine learning library for Apache Spark.
The library is very scalable. This library was created by apache, and it makes it easy for your machine learning computations to scale.
It’s easy and quick to set up and can be integrated easily with other machine learning tools. It is well-known as a great tool for creating machine learning applications and algorithms.
Spark MLlib provides machine learning programmers with the following machine learning algorithms:
- Dimensional Reduction
- Basic Statistics
- Feature Extraction
It’s a great Python library that enables the easy definition, evaluation, and optimization of mathematical expressions.
Its features for performing scientific calculations include:
- GPU support that makes it suitable for performing heavy computations.
- Supports integration with NumPy.
- Stable and faster evaluations of variables, including the complex ones.
- Supports creation of C code to perform mathematical operations.
Theano provides faster development of a number of machine learning algorithms. Popular deep learning libraries like Keras, Lagagne, and Blocks are built on top of Theano.
This is an efficient and flexible library for performing deep learning tasks in machine learning. If you want to perform deep learning tasks, MXNet is the perfect library to use.
MXNet supports quick model building and is a highly scalable library, making it a good library for training and deploying deep learning models.
Other than Python, the library can be used in other programming languages such as Perl, C++, R, Julia, Go, Scala, and many others. Due to its scalability and portability, you can move it from platform to another then scale it according to your project needs.
The education and tech giants like Microsoft, Intel, MIT, and others support MXNet. Amazon’s AWS also uses MXNet as the best deep learning framework.
Matplotlib is a Python library for performing data visualization tasks.
However, this library is not directly related to machine learning. It becomes useful to machine learning programmers when they want to visualize the patterns hidden in data.
It’s a 2D plotting library used for generating 2D graphs and plots. The library comes with a module named pyplot that makes it easy for programmers to create plots.
This module provides features that programmers can use to control line styles, format axes, font properties, etc. It also provides various plots and graphs for data visualization. Examples include bar charts, line charts, scatter plots, error charts, etc.
SciPy stands for Scientific Python. It’s a Python library for performing machine learning tasks.
The library comes with modules for image optimization, special functions, linear algebra, signal and image processing, ordinary differential equation solving, Fast Fourier transform, and other computational tasks related to science and analytics.
SciPy uses a multi-dimensional array data structure that is provided by the NumPy module. So, SciPy uses the array manipulation subroutines provided by NumPy.
SciPy was developed to work with NumPy arrays and provide efficient and user-friendly numerical functions.
This is what you’ve learned…
Machine learning is the science of programming computers to learn from data. It’s through machine learning that computers are able to perform tasks without receiving explicit instructions to do so.
Machine learning involves the creation and training of machine learning models. There are different types of machine learning libraries in Python. Machine learning is wide, hence, these libraries are used to perform different machine learning tasks.
Some of the libraries discussed in this article can be used in programming languages like C++ other than Python. Pandas and Matplotlib are not directly related to machine learning. The former is a library for data analysis while the latter is a library for data visualization.
Originally published at https://acodez.in on June 8, 2020.