In today’s tech-dominated world, everything revolves around data. Businesses use data to make informed business decisions and understand their customers. And with the rise of artificial intelligence (AI) and machine learning, businesses are now using data to make predictions, optimize operations, detect fraud, and much more.
You are probably wondering, what is machine learning exactly? Machine learning is a process in which computers learn from data to develop artificial intelligence. Data scientists are often the professionals who build and maintain these machine-learning models.
For many, the go-to language for model development is Python, due to its simplicity and extensive library support. In this article, we will introduce and explore the top 9 Python libraries used for machine learning.
What is Python?
Python is a popular language often used for programming web applications, conducting data analysis and scientific research, and building machine learning models. It was developed by a man named Guido van Rossum and first released in February 1991. Since then, Python development services has been expanding and now offers flexibility while maintaining its simplicity.
Why Python for Machine Learning & Natural Language Processing?
The data science community actively uses Python for machine learning (ML) and NLP needs. Some of the reasons Python has become the preferred language for any machine learning model include the following:
- Simple and clean syntax: Python is super simple to use—so much so, it is often the language of choice for new developers, researchers, and data scientists starting off in their careers. The syntax is clean and has built-in methods that are written in clear English, making it easier to follow and understand. Python libraries for ML and NLP are written with the same simplicity and ease of use, allowing professionals and beginners to quickly ramp up to the library.
- Extensive support for numerical computation: Python’s ML and NLP libraries offer built-in data structures, mathematical functions, and machine learning algorithms, making numerical computation easy and effective.
- Active community support and resources: Python is surrounded by a thriving community, which offers plenty of support and resources. This community offers guidance, answers questions, produces up-to-date documentation, and more, enabling everyone to have the necessary resources to succeed when using Python’s ML and NLP libraries.
- Wealth of ML and NLP libraries: Python has many well-known and robust ML and NLP libraries. Whether you are a beginner exploring data science or an experienced researcher delving into advanced AI projects, whatever project or initiative you have in mind, Python most likely has a library to support you and your project needs.
With all these benefits, it’s easy to see why Python is the language of choice for machine learning and NLP development—and why when it comes to machine learning libraries, Python shines.
Did you know that 30% of professionals prefer to use Python for development and that Python is used 90% of the time when data is involved?
If you’re as excited as we are, let’s jump into learning about some of the best Python libraries for machine learning available!
Top Machine Learning Libraries in Python
Before we get started, let’s understand what a library in Python actually is. A library is a collection of useful Python methods to help you achieve a goal. For example, if you need a Python sentiment analysis library, that library would most likely include everything you would need to perform sentiment analysis.
There are a variety of Python machine learning libraries available, including beginner-friendly options like Scikit-Learn to libraries that are more advanced. Let’s jump into the top 9 machine learning libraries in Python.
#1 Scikit-Learn
Scikit-Learn is a well-known free Python machine learning library. It also goes by the name “Sklearn” and is an open-source Python library. This library is the number one choice for a reason. It’s great for beginners who are new to machine learning because it offers pre-built models and even datasets like Iris to help you get started quickly. It also provides extensive support for features like preprocessing and cross-validation, making it a go-to option for advanced professionals as well.
Features
- Integrates well with other libraries like NumPy and Pandas
- Pre-built classification, regression, and clustering algorithms
- Provides utilities for data preprocessing, feature scaling, and feature extraction
- Offers techniques for dimensionality reduction and visualization of high-dimensional data
- Offers tools for model evaluation, hyperparameter tuning, and cross-validation
Use Case
Companies can improve their targeted marketing strategies by using Scikit-Learn to segment customers based on their purchasing behaviors.
#2 TensorFlow
TensorFlow is also a free machine learning Python library invented by the Google Brain team. After its release in 2015, it became a famous Python framework for creating deep learning models. The main purpose of this library is to help users build AI and deep learning apps. Tensorflow is often considered hard to learn and use since it requires a solid understanding of deep learning ideologies.
Features
- Able to run on multiple CPUs and GPUs
- Flexible and versatile for constructing and training different types of neural networks
- Automatic differentiation capabilities
- Can handle large datasets and high-dimensional data efficiently
- Enables efficient and scalable mathematical computations
- Allows model saving and serialization
- Offers a visualization tool for monitoring relevant metrics while training
Use Case
For recommender applications, TensorFlow provides the methods needed to build customized content and recommendations for customers on E-commerce platforms.
#3 Keras
Keras is an open-source Python library, also developed by Google. What makes Keras so popular is its tight integration with TensorFlow. It’s a neat neural network Python library built on TensorFlow. Keras provides a modular and user-friendly design, making it slightly more user-friendly than TensorFlow. And lastly, the intent of this library is to permit users to prototype, experiment, and productionize deep learning apps.
Features
- Designed to be user-friendly, modular, and extensible in nature
- Can leverage the backend of TensorFlow
- Supports other deep learning libraries
- Provides access to a collection of pre-trained models
- Includes a callback system used for model checkpointing
- Supports GPU acceleration
Use Case
In building reinforcement learning agents, Keras is used in the industry to optimize certain objectives, such as game playing or robotic control.
#4 PyTorch
PyTorch is an open-source deep learning framework based on the Torch library and written in Python. The Torch library is an ML and scientific computing framework written in Lua. This framework was created by Facebook’s AI research lab. PyTorch is generally used for deep learning applications like image recognition and language processing.
Features
- Highly flexible and extensible
- Enables the construction of dynamic computational graphs
- Native support for Python and seamless performance
- Automatic differentiation and optimization capabilities
- Enables serialization and deployment of models outside of Python
- Allows users to create custom layers, loss functions, and modules
Use Case
For computer vision tasks in the industry, PyTorch has been used for image classification, object detection, and more to teach computers how to see.
#5 Pandas
Pandas is a Python library used for data manipulation and analysis. This is another tool industry experts and beginners alike use. It provides helpful data structures, like Dataframes, and functions that aid users in handling their data. This makes it easier for users to prep their data for data analysis, model training, and testing.
Features
- Can read data from a variety of sources
- Can handle different types of data
- Provides convenient indexing and labeling
- Automatically aligns the data based on their labels
- Supports reading and writing data to and from various file formats
- Handles missing data, data alignment, merging, and joining of datasets
- Performs operations like slicing, indexing, and filtering to extract relevant data
Use Cases
In data preprocessing, Pandas is used to clean and transform raw data, preprocess it, and prepare the data for model training.
#6 NumPy
NumPy is a numerical computing library that’s useful for data science and machine learning work. This library has helpful array objects and math functions to aid in scientific computing and data analysis. NumPy also works well with other Python libraries.
Features
- Powerful N-dimensional array objects
- Broadcasting feature that allows operations between arrays with different shapes and dimensions
- Provides a comprehensive set of mathematical functions
- Users can integrate low-level code written in C, C++, or Fortran into Python
- Includes tools for generating random numbers
Use Case
NumPy is used in the background by libraries like Pandas, which relies heavily on NumPy arrays for handling and processing data. Pandas use NumPy’s fast and memory-efficient arrays to build its DataFrame and Series data structures.
#7 Matplotlib
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. This library is useful for creating helpful visualizations of data analysis, model outputs, and more. Many people use this library alongside the NumPy library to create the arrays for visualization purposes.
Features
- Offers the ability create a wide variety of plots and charts in different formats
- Integrates with Jupyter Notebooks
- Can be used to visualize data, an essential part of the machine learning process.
- Offers a diverse set of plot types, including line plots, scatter plots, bar plots and more
- Supports various output formats
- Includes interactive plots for Jupyter Notebooks that can be updated dynamically
- Integrates with NumPy arrays, making plotting easy
- Allows customization for visualization plots
Use Case
In machine learning, Matplotlib is used to display feature importance scores, which help identify the most influential features in a model’s predictions.
#8 Seaborn
Seaborn is a Python data visualization library based on Matplotlib. Users are able to make compelling and complex visualizations that are more aesthetically pleasing and statistically inclined. They are able to do this with less code when compared to Matplotlib. This library also works seamlessly with Pandas, making it extremely desirable for projects using Pandas.
Features
- Provides color palettes for data points
- Seamless integration with Pandas data structures
- Creates more attractive and informative statistical graphics
- Offers specialized plotting functions to visualize statistical relationships in data
- Allows users to create facet grids and excels at categorical plotting
Use Case
In exploratory data analysis, Seaborn is used before applying machine learning algorithms, helping data scientists and analysts explore and understand data before applying machine learning algorithms
#9 Theano
Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is useful for machine learning and deep learning applications, where large amounts of data need to be processed quickly. This is a symbolic math library, meaning it first compiles your mathematical expressions into legitimate C code. This allows Theano to run much faster than if it had to evaluate the expressions directly in Python.
Features
- Support for GPU execution, important for certain deep learning computations
- Tight integration with NumPy allowing users to work with NumPy arrays
- Generates optimized C code from the symbolic expressions defined by the user
- Automatically calculates gradients and derivatives of complex mathematical expressions using symbolic differentiation
- Automatically unroll loops and parallelize operations to optimize computation
Use Case
In scientific investigations, Theanos is used for computationally intensive tasks, such as simulations, numerical optimization, and solving differential equations. Its ability to generate efficient C code makes it suitable for handling large datasets and complex mathematical models.
The Bottom Line
Python has solidified its position as a leading programming language for machine learning and natural language processing due to its simplicity, extensive library support, and vibrant community. If you have a machine learning project in mind or would like to hire a machine learning development company, you can’t go wrong by starting with the top 9 Python libraries we have mentioned in this article. Keep in mind the project requirements, data size, and complexity when selecting libraries for your projects.
If you enjoyed this article, check out one of our other Python articles.
- Anaconda vs Python Programming Explained With Differences
- 3 Skills Any Beginner Python Developer Should Have
- Is Python the Right Tool to Help Your Company Visualize Data?
- 5 Best Python Data Visualization Libraries
- Python Poetry: A Poem for Python Dependency Management
FAQs
What makes Python a popular choice for machine learning?
Python is a popular choice for machine learning because of the unique advantages that the language offers. Not only is its syntax simple, ensuring readability, but it also supports descriptive and interactive code, which is beneficial for data exploration and algorithm development. Furthermore, the extensive library support and a large, active community make it even more appealing.
How do I choose the right library for my machine learning project in Python?
To choose the right Python library for your machine learning or data mining project, start by identifying your project needs. For example, if you plan to build a classification algorithm or delve into data mining techniques, it could help to narrow your search for libraries that support those requirements.
If you’re a beginner, you should also research the amount of support you could receive when using specific libraries. That way, if you come across any issues, you would have support from community members.
What are some example projects for understanding machine learning models?
Here are a list of common machine learning projects that you could explore to increase your understanding of machine learning models:
- Image Classification
- Sentiment Analysis
- Spam Email Detection
- Predictive Maintenance
- Stock Price Prediction
- Recommendation Systems
- Fraud Detection
- Natural Language Processing
- Handwritten Digit Recognition
- Autonomous Driving
Type in any of the projects listed above in your search engine, where you would find many example projects and explanations.
