The Best Python Libraries for Machine Learning and AI: Features & Applications

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Nicolas Azevedo
Data Scientist and Machine Learning Engineer
Illustration of popular Python libraries for machine learning and AI

Python is one of the most powerful and widely used languages in AI and ML development. Its rising popularity in artificial intelligence and machine learning projects is the result of its user-friendly syntax, flexibility, and most importantly, its rich library ecosystem. 

Python’s comprehensive libraries streamline tasks from data wrangling to algorithm development. Because ML requires continuous data processing, Python’s library ecosystem enables developers to access, manipulate, and transform data. 

Table Of Contents

In this article, we’ll explore essential Python libraries for AI and ML development, including Pandas, NumPy, and Matplotlib. We’ll also dive into ML tools like Sklearn, TensorFlow, and Keras. By the end, you’ll understand these tools and their specific applications in AI projects. Let’s start with an in-depth look at data manipulation libraries.

Six categories of python libraries for machine learning, including visualization and NLP

Python Libraries for Data Manipulation and Analysis

Pandas

Imagine you’re working with an e-commerce business that has massive databases containing user interactions, purchases, and a plethora of product details. They ask you to help extract monthly insights, such as identifying the top-selling products, pinpointing the highest-spending users, and calculating average sales per category. But there’s a problem; the dataset is messy, filled with typos, duplicated records, missing values, and inconsistent formats. 

Your first challenge in understanding this data isn’t just in the size, but in making sense of it all. The solution? Enter Pandas.

Pandas isn’t just another tool; it’s a powerhouse for data manipulation in Python, turning daunting tasks into manageable ones. It empowers you to delve into data with ease, offering robust functionalities for sorting, filtering, transforming, and grouping. Whether it’s performing aggregations like averages and sums or preparing comprehensive reports, Pandas streamlines these processes with minimal coding.

Originally published on Jan 16, 2024Last updated on Mar 1, 2024

Key Takeaways

What Python library is used for machine learning?

Many Python libraries are used for machine learning. Some of the most widely used libraries include Scikit-learn (or Sklearn) for simple and traditional tasks; TensorFlow and PyTorch; Keras as a high-level neural networks API; Pandas for data manipulation; NumPy for numerical operations; and Matplotlib/Seaborn for data visualization.

Is NumPy an ML library?

While NumPy was not designed specifically for machine learning, it is commonly used in ML projects. It is a foundational library used for numerical operations in Python. NumPy supports large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these elements.

Which is better, PyTorch or TensorFlow?

While it ultimately depends on your business needs, PyTorch has gained more popularity than TensorFlow and offers several advantages. This includes a dynamic computational graph that facilitates intuitive model development and debugging. Its Pythonic and user-friendly API makes it accessible for researchers and developers, while its popularity in the research community ensures a wealth of cutting-edge models and resources. PyTorch's flexibility and ease of use make it an excellent choice for prototyping and experimentation in machine learning projects. However, TensorFlow is still used in extensive projects with big deployment requirements.

Is TensorFlow better than Sklearn?

You can think of TensorFlow as the superhero for advanced jobs, while Scikit-learn is the friendly guide for basic tasks. TensorFlow is a comprehensive platform for crafting and honing complex neural networks, well-suited for handling hefty datasets and ensuring scalability in deployment. Its versatility spans multiple domains, from healthcare to finance, and from image and speech recognition to NLP tasks. On the other hand, Scikit-learn (Sklearn) is great for simpler tasks when your information is well organized. Sometimes, people use both for different parts of their projects.

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