Data Science
Unlock the Power of AI: Essential Python Libraries for Machine Learning and Data Science
Mastering Python Libraries to Supercharge Your AI and Data Science Skills
Python is the official programming language of the AI community. It’s easy to learn, and a writing program is a snap once you are proficient. Thanks to its large open-source libraries, Python users can manipulate data, prototype models, analyze outputs, and perform many other machine learning and data science tasks.
This post is aimed at people just beginning to use Python for AI as well as those who have experience but have questions about what to learn next. We’ll take a moment from time to time to fill in newcomers about basic terms and concepts. In this blog, we will get to know the most essential Python libraries and packages for AI, explain how to use them, and go through their strengths and weaknesses.
The Most widely used Python Libraries for AI and ML
Choosing the correct libraries for your development environment is critical. Most AI developers require the following packages and libraries. They are all publicly available as open-source distributions.
Scikit-learn: If you need to do Machine Learning
What is it?: Scikit-learn is a Python library used to implement machine learning algorithms.
Background: A developer named David Cournapeau initially developed Scikit-learn as a Google Summer of Code project. Originally released scikit-learn as a student in 2007. The open-source community quickly adopted it and has updated it numerous times over the years.
Features: The packages in Scikit-learn mainly focused on modeling data.
- Scikit-learn includes every core machine learning algorithm, including vector machines, random forests, gradient boosting, k-means clustering, and DBSCAN.
- It was designed to work seamlessly with NumPy and SciPy (described below) for data cleaning, preparation, and calculation.
- It has modules for loading data as well as splitting it into training and test sets.
- It supports feature extraction for text and image data.
- It allows Clustering for the grouping of unlabeled data.
Pros: Scikit-learn is a must-have for anybody working in machine learning. It is considered one of the best libraries available if you need to implement algorithms for classification, regression, clustering, model selection, and more.
- Providing a consistent interface for machine learning algorithms
- Offering many tuning parameters for each algorithm and using sensible defaults.
- Having excellent documentation.
- Having rich functionality for tasks related to machine learning.
Cons: Scikit-learn was built before deep learning took off. While it works great for core machine learning and data science jobs, if you are building neural nets you’ll need either TensorFlow or Pytorch.
- It is not the best choice for in-depth learning
The best resource to learn: Machine Learning in Python with Scikit-Learn from Data School.
NumPy: If you need to crunch numbers
What is it?: NumPy is a Python package for working with arrays or large collections of homogeneous data. Think of an array as a spreadsheet with numbers stored in columns and rows.
Background: Python was not originally developed for numerical computation when it was launched in 1991. Still, Its ease of use pulled the attention of the scientific community early on. Throughout the years, the open-source community developed a series of packages for numerical computing. In 2005, developer Travis Oliphant combined over a decade of open-source developments into a single library for numerical computation, which he called NumPy.
Features: The main feature of NumPy is its array support, which allows for quick processing and manipulation of large data collections.
- NumPy arrays can be n-dimensional. This means that the data can be as simple as a single column of numbers or as complex as many columns and rows of numbers.
- Some linear algebra functions can be performed using NumPy modules.
- It also includes graphing and plotting modules for numerical arrays.
- NumPy array data is homogeneous, which means it must all be of the same type (numbers, strings, Boolean values, etc.). This means that data is processed quickly.
Pros: Manipulating and processing data for more advanced data science or machine learning operations. If you are crunching numbers, you need NumPy.
- Numpy arrays significantly outspeed Python lists for numerical computations, allowing for faster data analysis and model training.
- Integrates seamlessly with other key libraries like Scikit-learn, pandas, and matplotlib, streamlining data workflows.
- Perform element-wise operations on arrays of different shapes, simplifying complex calculations and code conciseness.
- Built-in function for common mathematical operations like linear algebra, trigonometry, and statistics, reducing dependencies on external libraries.
Cons: Because NumPy arrays are homogeneous, they are a bad fit for mixed data. You are better off using Python lists. Also, NumPy’s performance tends to drop off when working with more than 500,000 columns.
- Indexing errors can be cryptic and lead to unexpected behavior due to contiguous memory allocation.
- While workarounds exist, NumPy arrays lack native support for missing values (NaNs), which can be crucial for data analysis.
The best resource to learn: Python NumPy Tutorials for beginners by freeCodeCamp.
Pandas: If you need to manipulate data
What is it?: Pandas is a Python library for working with labeled data, such as CSV files containing different types of data.
Background: Wes McKinney created Pandas in 2008. It is built on top of NumPy, which must be installed to use Pandas, and it extends NumPy’s functionality to work with heterogeneous data.
Features: The core feature of Pandas is its variety of data structures, which let users perform an assortment of analysis operations.
- Pandas has a variety of modules for data manipulation, including reshape, join, merge, and pivot.
- Pandas has data visualization capabilities.
- Users can perform mathematical operations including calculus and statistics without calling on outside libraries.
- It has modules that help you work around missing data.
Pros: Data analysis.
- Built-in functions for statistical analysis, time series analysis, and data visualization simplify data exploration and insights generation.
- Seamlessly integrates with Numpy, Scikit-learn, Matplotlib, and more enabling smooth data science workflows.
- Pandas excel at cleaning, manipulating, and exploring data through indexing, filtering, aggregation, and merging operations.
- Pandas operations often replace complex loops with concise and expressive code, improving readability & maintainability.
Cons: Switching between vanilla Python and Pandas can be confusing, as the latter has a slightly more complex syntax. Pandas also have a steep learning curve. These factors, combined with poor documentation, can make it difficult to pick up.
- For massive datasets, Pandas operations can become slow, requiring alternative libraries like Spark or Dask for distributed libraries.
- Pandas primarily shine with structured, tabular data, Unstructured data like text or mages might require specialized libraries.
The best resource to learn: Pandas for Data Science by Nicholas Renotte.
SciPy: If you need to do math for data science
What is it?: SciPy is a Python library for scientific computing and technical computing. It contains packages and modules for performing calculations that help scientists conduct or analyze experiments.
Background: In the early days of computers, when dial-up was king and floppy disks held mysteries, some clever programmers wanted to make science easier with Python. They built a powerful library called NumPy, and from there grew SciPy, a kind of super-tool for tackling scientific problems. SciPy can crunch numbers like nobody’s business, solve riddles hidden in equations, and even make statistics sing! Today, it’s used for everything from physics to finance, and it’s free and open to everyone. So if you’re curious about the world and have some data dancing around, let SciPy work its magic and help you discover amazing things!
Features: SciPy’s packages comprise a complete toolkit of mathematical techniques from calculus, linear algebra, statistics, probabilities, and more.
- Some of its most popular packages for data scientists are for interpolation, K-means testing, numerical integration, Fourier transforms, orthogonal distance regression, and optimization.
- SciPy also includes packages for image processing and signal processing.
- The Weave feature allows users to write code in C/C++ within Python.
- SciPy has math & stats features to crunch numbers, analyze data with tests and curves, and generate randomness for experiments.
Pros: SciPy is a data scientist’s best friend.
- Optimized code and parallel processing capabilities allow SciPy to handle large datasets and computationally intensive tasks without breaking a sweat.
- Plays well with other data science libraries, making your workflow smooth and efficient.
- Due to its Open-source and Community-driven constant updates and improvements from a vibrant community SciPy stays cutting-edge and relevant.
Cons: Some users have found SciPy’s documentation lacking and critique several of its packages as inferior to similar packages found in MatLab.
- SciPy for basic data analysis or manipulation, SciPy’s powerful tools might be overkill, potentially slowing down your workflow.
- SciPy integrates well with other libraries, but it’s not ideal as a first step for absolute beginners, NumPy or pandas might be easier initial choices.
The best resource to learn: SciPy in Python by Great Learning.
If you are interested in Machine Learning, I suggest you consider learning it.
TensorFlow vs PyTorch
TensorFlow and PyTorch are libraries that facilitate tasks related to deep learning such as data acquisition, model training, and prediction generation. These libraries are used to code several neural networks, including those used for face recognition and language models. Previously, the two libraries had marked differences both in the front and back end. However, they have since converged around the same set of best practices over time.
Debate continues in the AI community over which platform is best. TensorFlow, which was released in 2015, was the first one to arrive. It dominates commercial AI and product development, but many users find it too complex.
PyTorch, released in 2016, is generally considered easier to learn and faster to implement than other deep learning frameworks. However, it is known to struggle with scaling.
Which one to choose?
TensorFlow remains the most widely used deep-learning library in the industry. This can be attributed to a combination of factors such as inertia, as well as TensorFlow’s superior ability to handle large and complex workflows when compared to PyTorch. Its proficiency in managing AI projects for commercial deployment makes it a popular choice for product development.
If you’re new to deep learning and want to quickly build and prototype models, PyTorch is likely the better choice. However, keep in mind that depending on your company’s technology and job requirements, you may need to learn TensorFlow in the future. This is especially true if you’re aiming for a job at Google, which uses TensorFlow as its primary deep-learning framework.
Learn more about the pros and cons of both libraries below.
TensorFlow
What is it? TensorFlow is a free, open-source library for building and training machine learning models, particularly focusing on deep neural networks. It simplifies data processing, model creation, and deployment, making complex AI accessible to a wider audience.
Background: TensorFlow was created by Google’s Brain team in 2015 to meet their research and production needs. It quickly progressed from an internal tool to an open-source library in 2017, allowing developers worldwide to build and use advanced deep-learning models. Nowadays, it is widely employed to drive research, fuel innovative applications, and make AI easily accessible to everyone thanks to its user-friendly and flexible features.
Features: TensorFlow has numerous packages for building deep learning models and scaling them for commercial deployment.
- Users of TensorFlow have access to hundreds of pre-trained models through the Dev Hub and Model Garden. The Dev Hub offers plug-and-play models, while the Model Garden is designed for advanced users who are comfortable making customizations.
- It is possible to train multiple neural networks in parallel due to its efficient use of memory.
- TensorFlow applications are compatible with a range of hardware systems, such as CPUs, GPUs, TPUs, and more.
- TensorFlow Lite is a machine learning framework designed to work efficiently on mobile and embedded devices. It is optimized to run machine learning models smoothly on devices with limited computational resources.
Pros: Building production-ready deep learning models at scale.
- Built for constricting and training deep neural networks, making it a go-to choice for tasks like image recognition and language processing.
- Leverage multi-dimensional arrays (“tensors”) for efficient computation, simplifying complex mathematical operations.
- Runs on various hardware platforms (CPUs, GPUs)and handles large datasets with optimized code and distributed processing.
- Integrates with other libraries like TensorFlow Hub and keras, providing pre-trained models and building blocks for faster development.
Cons: Some users still complain that the front end is fairly complicated. You may also come across critiques that TensorFlow executes slowly. This is mostly a legacy complaint from TensorFlow 1.0 when it executed operations in graph mode by default. TensorFlow 2.0 defaults to eager execution mode.
- TensorFlow’s powerful features come with a demanding learning curve. Its complex syntax, data flow graphs, and deep learning focus can overwhelm beginners or users unfamiliar with machine learning concepts.
- Running TensorFlow on CPUs can be significantly slower than on GPUs, specifically designed for efficient parallel processing of matrix operations common in deep learning.
The best resource to learn is the TensorFlow Developer Professional Certificate from DeepLearning.ai.
Keras
What is it?: Keras is a user-friendly tool for building neural networks. It serves as the interface for TensorFlow.
Background: In 2015, Francois Choillet, a Google engineer, created Keras as an API for various deep-learning libraries. Initially designed for Theano, it quickly evolved into a versatile API that can work with multiple frameworks, including TensorFlow. Today, Keras is a popular high-level deep learning library that acts as a lego set for assembling neural networks with ease. It simplifies complex coding tasks, allowing you to focus on the core of your project — the exciting world of machine learning! As of 2020, Keras is now exclusive to TensorFlow.
Features: Keras simplifies building neural networks in TensorFlow with essential modules like activation functions, layers, optimizers, and more.
- Keras supports vanilla neural networks, convolutional neural networks, and recurrent neural networks as well as utility layers including batch normalization, dropout, and pooling.
- It is designed to simplify coding deep neural networks.
- Build your networks like Lego! Keras provides pre-built layers and modules for various tasks (convolution, pooling, recurrent, etc.), letting you snap them together into your desired architecture.
Pros: Developing deep learning networks.
- Keras’s intuitive API and concise syntax make It easier to code neural networks, letting you focus on the bigger picture of your project.
- Quickly test and iterate on your models with Keras’s expressiveness. This fast feedback loop accelerates your development and learning process.
- Keras plays well with popular frameworks like TensorFlow and PyTorch. This flexibility lets you leverage their underlying power while enjoying Keras’s user-friendliness.
Cons: If you are a user of TensorFlow, then you are also a user of Keras, as Keras is only available for TensorFlow users.
- Troubleshooting issues in complex Keras models can be tricky due to the abstraction layers. This can be frustrating for beginners or when dealing with unexpected errors.
- While Keras simplifies coding, it also abstracts away some underlying details of your model. This can limit your ability to fine-tune specific aspects for advanced users.
The best resource to learn: Introduction to Deep Learning and Neural Networks with Keras from IBM.
PyTorch
What is it?: PyTorch is a general-purpose open-source library for deep learning created by Facebook AI Research Lab as an alternative to TensorFlow, designed for machine learning and data science.
Background: PyTorch was developed in 2016 at Facebook AI and is a robust deep-learning library that provides flexibility and power. With dynamic construction instead of static graphs, this library allows you to shape your models like clay with the help of Python language. It quickly became a popular framework due to its ease, flexibility, and supportive community, making it a go-to playground for AI creators of all levels.
Features: PyTorch and TensorFlow have similar features. Both libraries have incorporated the best features of each other since their launch.
- PyTorch has its libraries for pre-trained models. The PyTorch Hub is aimed at academic users who want to experiment with the model design, and the Ecosystem Tools contains pre-trained models.
- PyTorch is a memory-efficient framework that allows for training multiple models in parallel. It is designed to optimize the use of available system resources and facilitate the development of complex machine-learning models.
- It is capable of supporting a diverse range of hardware types.
Pros: Rapid prototyping of deep learning models. Pytorch code runs quickly and efficiently.
- Think of it as a programming playground for AI, offering dynamic computation and a Pythonic approach for intuition model creation.
- Familiar Python syntax makes code easy to learn and intuitive, especially for existing Python users.
- See the results of your code line by line, aiding debugging and making development iterative and flexible.
- Vibrant community provides support, and tutorials and contributes to continuous development.
Cons: Some users have reported that PyTorch struggles with larger projects, big datasets, and complex workflows. As a result, developers who build AI products to be deployed at scale may prefer to use TensorFlow instead.
- PyTorch has fewer ready-made tools and libraries for deployment and optimization compared to TensorFlow.
- When dealing with large datasets, eager execution can consume a lot of memory, which calls for efficient resource management to avoid any memory-related issues.
The Best resource to learn: PyTorch tutorials from PyTorch.org.
Conclusion
Python’s popularity among the AI community can be attributed to the maturity of its libraries. These libraries make it effortless to use Python for tasks beyond its original design. Once you have a good knowledge of Python language and the libraries that are relevant to your work, you can easily build, train, and refine machine-learning models for a wide variety of applications.
While Python is popular and it comes with several libraries, it may not be the ideal choice for every task. If you are working with AI infrastructure, learning C++ may be necessary. However, in contrast, if you are working in the finance industry, learning R may be helpful.
No matter what your AI goals are, the best thing to do is always keep learning!
