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Artificial Intelligence (AI) and machine learning (ML) are gaining increasing traction in today’s digital world.
This popularity is why IT professionals should master concepts such as machine learning tools and machine learning algorithms.
Today, we are exploring machine learning libraries, specifically Python machine learning libraries. We offer a preview of the top libraries that ML professionals will be using in 2023 so that people can get a jump on the new year.
We begin with a few definitions to make sure everyone’s up to speed.
What Is Machine Learning?
Although it’s tempting to conflate AI and ML, they are two distinct concepts.
- What is Artificial Intelligence? Artificial Intelligence (AI) is the process of programming machines to simulate human intelligence by thinking like humans, imitating their actions, and making decisions.
- What is Machine Learning? Machine learning (ML) is a subset of AI involving the study of computer algorithms that allow computers to learn and grow from the experience, apart from human intervention.
In summary, AI is a catch-all term for teaching machines how to think and accomplish tasks like humans, and ML is a type of AI where computers receive data and learn on their own.
What Exactly Is a Machine Learning Library?
In the deep, dark, ancient days of early machine learning, programmers conducted ML tasks by coding the statistical and mathematical formulae and every algorithm by hand.
This approach was time-consuming, inefficient, and tedious.
Today, libraries, modules, and frameworks handle those monotonous tasks. Libraries contain modules and codes that provide system functionality and standardized solutions for most everyday programming problems and issues.
Libraries make it easy for organizations to benefit from the countless machine learning applications without wasting time and resources.
Why Learn About Python Machine Learning Libraries?
Python is considered one of the fastest-growing programming languages, outdistancing others such as Java, JavaScript, C#, and PHP.
Programmers love Python due to its simplicity and readability. Consequently, a machine learning engineer who wants to create smart algorithms for machines turns to tools that make it easy for the device to understand.
That’s where Python comes in.
This simplicity makes sense when you consider that the best way to teach a person a new language or subject is by using basic, easy-to-understand words and phrases.
Clearly, machines are no different.
Here’s a summary of why you might want to learn about Python machine learning libraries.
- It’s free and open-source, making it community-friendly, which in turn guarantees a constant flow of improvements in the long run
- It has exhaustive libraries that ensure you can find a solution for every existing problem
- Its smooth implementation and integration make it accessible for people of any skill level to adapt to it
- It increases productivity by reducing coding and debugging times
- It’s useful for soft computing and natural language processing
- It works seamlessly with C and C++ code modules
A Look at 2023 Top Python Machine Learning Libraries
Machine learning is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learning.
Thanks to scientific Python and its libraries, modules, and frameworks.
Python machine-learning libraries have become the most preferred language for machine-learning algorithm implementations.
Learning Python is essential to mastering data science and machine learning. Let's look at the main Python libraries used for machine learning.
Top Python Machine Learning Libraries
NumPy
NumPy, a well-known package for general-purpose array processing, is very popular. NumPy is able to process large multi-dimensional arrays or matrices thanks to its extensive library of complex mathematical functions.
NumPy can handle linear algebra, Fourier transforms, and random numbers. NumPy is used by TensorFlow and other libraries to manipulate tensors.
NumPy allows you to define any data type and integrate it with almost all databases. NumPy is also a multi-dimensional container that can hold any generic data of any type.
NumPy's key features include:
- Powerful N-dimensional array objects
- Broadcasting functions.
- You can integrate C/C++ or Fortran code using out-of-the-box tools.
The key Features of are Listed Below:
- Supports n-dimensional arrays for vectorization, indexing and broadcasting operations.
- Fourier transformations of mathematical functions, linear algebra methods and random number generators.
- It can be used on a variety of computing platforms, including GPU and distributed computing.
- High-level syntax that is easy to use and optimized for Python code, which allows you to be flexible and speedy.
- NumPy also allows the computation of numerical python operations in many libraries related to data science, data visualization and image processing. So, it is one of the versatile machine-learning libraries.
SciPy
Many Python program developers created Python libraries for machine learning, particularly for scientific computing and analytical computing, as machine learning models were growing at an incredible rate.
SciPy's current development is supported by and sponsored openly by a community of developers. It is distributed under the BSD license.
SciPy provides modules for image optimization, integration interpolation, and linear algebra. It also offers special functions, such as fast Fourier transforms, signal and picture processing, ordinary differential equation solving, and other computational tasks related to science and analytics.
SciPy uses a multi-dimensional array provided to it by the NumPy module as its underlying data structure. SciPy relies on NumPy to perform array manipulation subroutines.
SciPy was designed to work with NumPy arrays, and provide efficient and user-friendly numerical functions.
SciPy's unique feature is its ability to be used in math and other sciences. It is used for optimization functions, signal processing, and statistical functions.
It also supports functions to find the numerical solution to integrals. You can use it to solve optimization and differential equations.
SciPy's applications are categorized as the machine-learning libraries.
- Multidimensional image processing
- Fourier transforms and differential equations are solved
- You can use its optimized algorithms to perform linear algebra calculations efficiently and reliably.
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Scikit-learn
As part of the Google Summer of Code 2007 project, David Cournapeau developed the Scikit-Learn Library. INRIA participated in the development of the Scikit-learn library and released it publicly in January 2010.
Scikit-learn was developed on top of two Python libraries, NumPy and SciPy. It has since become the most widely used Python machine learning library to develop machine learning algorithms.
Scikit-learn offers a variety of unsupervised and supervised learning algorithms. It works with a consistent interface in Python.
This library can be used to perform data mining and analysis. The Scikit-Learn library is capable of handling the following machine-learning functions: classification, regression and clustering; dimensionality reduction; model selection; preprocessing; and dimensionality reduction.
Scikit-learn is used by many data scientists and ML enthusiasts. It is essentially an all-inclusive framework for machine-learning.
It is sometimes overlooked due to the availability of better Python libraries and frameworks. It is still a powerful library that efficiently solves complicated machine learning tasks.
Sci-kit-learn is one of the most popular machine learning libraries in Python.
- It is easy to use for accurate predictive data analysis
- This simplifies complex ML problems such as classification, preprocessing and clustering, regression, model selection, dimensionality reduction, and regression.
- There are many machine learning algorithms built in
- This course helps you build a ML model from the ground up.
- This was built on top of popular libraries such as SciPy, NumPy and Matplotlib.
Read More: What Is Machine Learning? Different Fields Of Application For ML
Theano
Theano is an optimized compiler that uses Python to evaluate and manipulate mathematical expressions and matrix calculations.
Theano is built on NumPy. It has a similar interface and tight integration with NumPy. Theano is compatible with both graphics processing units and CPUs.
The GPU architecture produces faster results. Theano can do data-intensive computations up 140x faster than a CPU on GPU.
Theano is able to automatically avoid bugs and errors when dealing with exponential and logarithmic functions. Theano includes unit testing and validation tools that can be used to avoid bugs and other problems.
Theano's speed gives you a competitive advantage over C projects when it comes to solving problems that require large amounts of data.
Most GPUs can perform better than C on a CPU.
It can efficiently take structures and convert them into very efficient code using NumPy and some native libraries.
It is designed to handle various computations required by Deep Learning's deep neural network algorithm. It is therefore one of the most popular machine-learning libraries in Python for "deep learning."
These are the top benefits of using Theano
- Stability Optimization It can identify unsteady expressions, and can use steady expressions to solve them
- Execution Speed OptimizationIt implements part of your GPU or CPU's expressions. It is therefore faster than Python.
- Symbolic Difference:It automatically generates symbolic graphs to calculate gradients.
TensorFlow
The Google Brain team developed TensorFlow for internal use. The Apache License 2.0 was the first release of TensorFlow.
It was released in November 2015. TensorFlow is a popular computational tool for building machine deep learning models. TensorFlow can be used to build models at different levels of abstraction using a variety of toolkits.
TensorFlow exposes stable Python and C++ APIs. Although it can expose backwards-compatible APIs for other languages they may be insecure.
TensorFlow's flexible architecture allows it to run on a variety of computational platforms, including CPUs, GPUs and TPUs. TPU is the Tensor Processing Unit, a hardware chip that was built around TensorFlow to support machine learning and artificial Intelligence.
TensorFlow powers some of the most advanced AI models in the world. It is also recognized as an end to end Deep Learning and Machine Learning library that solves practical problems.
TensorFlow is one of the most powerful machine-learning libraries available in Python.
- Complete control over the development of a machine learning model as well as a robust neural network
- TFX, TensorFlow.js and TensorFlow Lite allow you to deploy models on the cloud, mobile, edge, or web.
- Many extensions and libraries available to solve complex problems
- Different tools are available to integrate Responsible AI and ML models.
Keras
Keras had over 200,000 users in November 2017. Keras is an open source library that can be used for convolutional neural networks and machine learning.
Keras can be run on top of TensorFlow and Theano as well as Microsoft Cognitive Toolkit R or PlaidML. Keras can also run on a GPU or CPU.
Keras is a tool that works with neural-network building blocks such as layers, objectives, and activation functions.
Keras has a lot of features that allow you to work with images and text images. This is useful when writing deep neural network code.
Keras supports both convolutional as well as recurrent neural networks in addition to the standard neural network.
It was released in 2015. Now, it is an open-source Python deep-learning framework and API. In many aspects, it is similar to TensorFlow.
It is human-based and aims to make DL/ML easy and accessible for everyone.
Keras is one of the most versatile machine-learning libraries in Python, because it contains:
- TensorFlow offers everything, but it presents it in an easy-to-understand format.
- Quickly executes multiple DL iterations with full deployment capabilities.
- Large TPU and GPU clusters are available to support commercial Python machine-learning.
- It's used in a variety of applications including natural language processing and computer vision. It is also useful for graph, structured and audio data.
Read More: Benefits of Python Development Language for AI and ML
PyTorch
PyTorch provides a variety of libraries and tools that support machine learning, computer vision, and natural language processing.
PyTorch is an open-source library that is built on the Torch library. The PyTorch library's greatest advantage is its simplicity of use and learning.
PyTorch integrates seamlessly with the Python data science stack, NumPy included. It is difficult to discern a difference between PyTorch and NumPy.
PyTorch allows developers to compute on tensors. PyTorch provides a solid framework for creating computational graphs and changing them during runtime. PyTorch also supports multi-GPU, simplified preprocessors and custom data loads.
In 2016, Facebook introduced PyTorch, a powerful competitor to TensorFlow. It is now a hugely popular tool for deep learning and machine-learning researchers.
PyTorch is a Python library that excels in machine learning. These are just a few of the key features.
- Support the creation of deep neural networks customized to your needs
- TorchServe is ready for production
- Distributed computing via the torch.distributed backend
- Multiple extensions and tools are available to help you solve complex problems.
- Compatible with all major cloud platforms for extensible deployment
Pandas
Pandas has become the most widely used Python library for data analysis. They support fast, flexible and expressive data analytics structures that can work with both "relational" and "labeled" data.
Python Pandas is a library that solves real-world data problems in Python. Pandas is stable and provides high-performance performance. Backend code is written entirely in Python or C.
Two Main Data Structures Pandas Use Are
- Series (1-dimensional)
- DataFrame (2-dimensional)
Together, these two can handle the vast majority of data needs and use cases in most sectors, including science, statistics, and finance.
Pandas can Work With Different Types of Data
- Tabular data that contains columns of heterogeneous information. Consider, for example, the data from an Excel spreadsheet or SQL table.
- Data for ordered and unordered time series data. Unlike other libraries and tools, the frequency of time series data does not need to be fixed. Pandas excels at handling data with irregular time-series.
- Arbitrary matrix data with heterogeneous and homogeneous data in the columns and rows
- Any other type of observational or statistical data. Data need not be labeled at all. Pandas can process data without labels.
In 2009, it was made open-source by Python. Many ML enthusiasts have made it their favorite Python library for machine-learning.
It offers robust data analysis and manipulation techniques. This library is widely used in academia. It also supports commercial domains such as web analytics, business analytics, statistics, finance, marketing, and neuroscience.
It can also be used as a foundational Python library.
These are its most important features:
- Missing data handled
- Handles time series data
- Supports indexing and slicing of large datasets.
- Optimized code for Python using C or Cython
- Data manipulation support via Powerful DataFrame objects
Matplotlib
Matplotlib is a data visualization tool that allows you to plot 2D images and figures in various formats. This library allows you to create histograms, plots, error charts, scatter plots, and bar charts using just a few lines of code.
It has a MATLAB-like interface that is extremely user-friendly. It makes use of standard GUI toolkits such as GTK+, Tkinter, wxPython, and Tkinter to provide an object-oriented API that allows programmers to embed graphs or plots in their applications.
It is the oldest Python library for machine learning. It is still not available. It is one of the most advanced data visualization libraries for Python.
It is a favorite of the ML community.
These are the key features of Matplotlib, making it a well-known Python machine learning library in the ML community.
- Interactive charts and plots make it easy to tell fascinating stories with data.
- This site offers a large selection of plots that are suitable for specific uses.
- Plots and charts can be customized and exported to a variety of file formats
- Different GUI applications can be integrated with embeddable visualizations
- Matplotlib is extended by a variety of Python programming language libraries and frameworks
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Conclusion
The preferred language for data science and machine learning is Python. There are many reasons to choose Python for data science.
Python is home to a large community of developers who create libraries for their purposes and then release them to the general public.
These are the most common machine-learning libraries that Python developers use.
