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Artificial intelligence (AI) and machine learning (ML) are both popular fields in software development that have seen significant growth in recent years. This growth is expected to continue with the rise of generative-AI tools like ChatGPT and DALL-E. Python, which hosts an extensive array of AI and ML libraries, is viewed by many to be the programming language of choice for developer AI-enabled software. With that in mind, this programming tutorial will highlight the best AI, ML and deep learning Python libraries that programmers, data scientists, and researchers can use to build intelligent applications and solve complex problems.
Python has a long history, during which it has grown from a general-purpose language to a highly flexible and evolved one that naturally lends itself to applications involving scientific computing, data analysis, and machine learning. With a clean, concise, and highly readable syntax and a large developer ecosystem of libraries, frameworks, and tools, Python is the perfect option for AI and ML software projects.
Some of Python’s key features that make it so ideal for AI include the following:
You can learn more about Python’s role in AI development by reading our tutorial: Benefits of Python for AI.
In the section below, we highlight some of the top Python libraries for AI, ML, and deep learning, including:
Scikit-Learn, also known as sklearn, is a highly regarded machine learning library that offers a huge array of tools for various ML tasks. It was built on top of several other popular Python libraries, including NumPy, SciPy, and Matplotlib, and affords developers a single interface for ML algorithms.
Among Scikit-Learn’s rich set of features include:
Scikit-Learn makes building and evaluating ML models simple, thanks to a workflow that mirrors the following:
Scikit-Learn finds use in a variety of real-world applications and industries, including:
TensorFlow was developed by Google as an open source deep learning framework. It is known to be highly flexible, scalable, and supportive for neural networks and deep neural networks. It features a computation graph model for defining and training complex neural networks with great efficiency.
Among TensorFlow’s capabilities in the realm of deep learning include:
In addition, TensorFlow also integrates with Keras (as of version 2.0), a high-level neural network API programmers can use to build and train deep learning models based off of Keras’s simple syntax without needing to switch to a separate backend environment.
TensorFlow has a great ecosystem for deploying models to production environments, making it ideal for real-world applications which include:
PyTorch is another popular deep learning framework. It is famous for its flexibility and dynamic computation graph. Created by Facebook’s AI Research lab (FAIR), PyTorch is much loved among research teams and is widely used in academia circles.
PyTorch features a dynamic computation graph that lets developers create flexible model constructs and provides easier debugging utilities. Its dynamic nature makes it well-suited for research and experimentation tasks, as programmers and researchers can modify network architectures on-the-fly.
PyTorch has a very user-friendly API that can be used for building and training neural networks. Its main features include:
PyTorch also has deployment and production capabilities. Programmers can use TorchScript to convert PyTorch models into deployable formats and the PyTorch Mobile Library lets you deploy models to mobile devices.
PyTorch is well known in the deep learning and research community, which has benefits its upkeep and maintenance. Its applications in real-world settings revolve around usage in:
NLTK is a library used for Natural Language Processing in Python. It features tools for many NLP tasks, including tokenization, stemming, lemmatization, part-of-speech tagging, and others. NLTK also offers a wide range of lexical resources for research and experimentation purposes.
spaCy, for its part, is known as a highly efficient, production-ready NLP library for Python. It is quick and simple to use, making it a good choice when you need to process large volumes of text data in real-time settings. spaCy has features like tokenization, named entity recognition (NER), dependency parsing, and text classification.
NLTK and spaCy both excel at text preprocessing and analysis tasks, including the following:
Finally, NLTK and spaCy both offer sentiment analysis functions you can use to determine sentiment and emotion that is expressed in text. This works well for social media monitoring applications and customer feedback.
OpenCV is a Python library used for computer vision tasks. It features a large collection of tools and algorithms for image and video processing tasks, making it a valuable library for AI and ML programmers that want to incorporate visual elements (like facial recognition).
OpenCV has the following primary features for image and video processing:
OpenCV is not simply used for image and video processing; it also has applications in robotics and autonomous systems (such as self-driving cars). Developers can equip robots with cameras and use OpenCV for tasks like navigation, avoiding obstacles, and manipulating objects.
XGBoost (also known as Extreme Gradient Boosting) is a Python machine learning library designed for gradient boosting, which is an ensemble learning technique. It is known for its efficiency and effectiveness in several machine learning competitions and real-world applications. The library builds its models using the predictions of multiple decision trees, enhancing predictive accuracy and generalization.
In this programming tutorial, we highlighted some of the top artificial intelligence and machine learning libraries for Python. We learned not only about the libraries and how they operate, but also there real world use cases.
Now that you have read about some of the top Python AI and ML libraries, we recommend you check out our tutorial: AI with Python: A Comprehensive Guide.