Owing to its simplicity, readability and a vast collection of libraries and frameworks, Python has become the language of choice for machine learning. Whether the requirement is to handle data more efficiently or creating machine learning algorithms, Python is used extensively.
If you are considering enhancing your machine learning skills, having a good understanding of Python Machine Learning Libraries is essential. Here we look at nine of the best Python Machine Learning Libraries you should know. â€
What Are Python Machine Learning Libraries?
Python Machine Learning Libraries are collections of pre-built modules and frameworks that facilitate the training, development and deployment of machine learning algorithms. With the use of these machine Learning libraries, development time is reduced as the developers are not required to implement codes from scratch.
Python is a preferred language for machine learning because of its English-like commands and syntax. It’s accessible, versatile, and portable and can be implemented rather easily across platforms.
Role of Python Machine Learning Libraries in Machine Learning
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Now let’s look at how Python Machine Learning Libraries aid in machine learning:
Standardization: Python machine Learning libraries ensure consistency and interoperability across projects and teams as they establish a common framework and set of conventions for ML tasks.
Accessibility: Owing to its simplicity and readability, Python is a widely accessible language for both beginners and experienced developers. ML libraries, which are built on Python, leverage these characteristics making it easy for learners to make a start in AI/ML.
Scalability: With Python machine learning libraries, it becomes easier for processing of large datasets and the training of complex models across distributed computing environments.
Community Support: Python has several communities of developers, researchers, and enthusiasts that contribute to the ongoing development and improvement of Python ML libraries.
Experimentation and Research: Researchers and data scientists can experiment with Python machine learning libraries and frameworks in a flexible and exploratory manner. This instills innovation and discovery in the field of machine learning.
Deployment: Python machine learning libraries come with built-in tools and utilities that deploy trained models into production environments. This allows organizations to leverage AI/ML capabilities within their applications and systems.
Also read: 10 Reasons to Learn Python in 2024
9 Best Python Machine Learning Libraries
Python comes with thousands of inbuilt libraries. Here we present 9 best machine learning libraries that have gained widespread adoption with the data science, machine learning and artificial intelligence communities.
Their unique strengths and use cases make them an ideal choice among developers, data scientists and researchers to deploy them and build robust and scalable machine learning solutions.â€
Tensorflow:
- Tensorflow is an open-source library from Google. Anyone can use, modify and distribute its source code.
- Owing to its flexibility, extensive documentation, and active community support, it is widely used in academic research and industrial applications.
- It is used to develop and deploy machine learning models for various practical applications including image and speech recognition, recommendation systems, fraud detection, etc.
- It supports various platforms making it ideal for deploying complex deep learning models.
Scikit-learn:
- A versatile and robust Python machine learning library known for its simplicity and efficiency
- Supports algorithms like logistic regression, support vector machines (SVM), k-nearest neighbors (KNN), decision trees, and random forests
- Comes built with data preprocessing tools and functions to handle missing values, encode categorical variables, improve model performance and reduce complexity
- Seamless integration with other libraries for numerical and data manipulation operations.
Pytorch:
- A highly extensible, open-source Python machine learning library originally developed by Facebook’s AI Research lab (FAIR) and based on Torch library.
- The module (torch.nn) that Pytorch defines provides a high-level interface for building neural networks.
- PyTorch supports Compute Unified Device Architecture (CUDA), NVIDIA’s parallel computing platform, that enables smooth GPU acceleration.
- PyTorch also includes utilities for handling datasets and data loaders
With PyTorch, users can easily define custom layers, loss functions, and optimization algorithms
NumPy:
- NumPy (Numerical Python), is a Python machine learning library which is used for working with arrays with its powerful N-dimensional array object being its core feature
- It performs element-wise operations on arrays, through its vast library of mathematical functions including trigonometric, statistical, algebraic, and other complex mathematical operations.
- NumPy uses the broadcasting feature to perform operations on arrays of different shapes and sizes without the need of replication
- NumPy performs random number generation with a suite of tools and sampling from various probability distributions.
Pandas:
- Pandas is an open-source Python machine learning library that facilitates data manipulation and analysis.
- It’s easy to use and comes with powerful features making it a widely used library in data science, finance, economics, statistics and other fields.
- It comes with a set of tools for cleaning and preprocessing data including dropna(), drop_duplicates(), map() and apply() which makes it easier to work on incomplete data sets.
- Pandas comes with inbuilt support for various methods for selecting, indexing, and filtering data including label-based, integer-based and boolean indexing.
Also read: Adding a New Column to an Existing Dataframe in Pandas
Keras
- Developed by Google, Keras is a high-level deep learning API for implementing high-level neural networks.
- With Keras one can perform distributed training of deep-learning models on clusters of graphics processing units (GPU) and tensor processing units (TPU)
- Depending on the user needs, Keras also provides a variety of deployment options (including Flask, FastAPI, and TensorFlow)
- Keras runs smoothly on both CPU and GPU, has built-in support for almost all neural network models and is expressive, flexible, and apt for innovative research.
Matplotlib
- Matplotlib is a static, open-source Python machine learning library that aids in data visualization, model performance and various other aspects of machine learning.
- It comes with a large ecosystem of add-on toolkits and extensions, making it a highly extensible machine learning library.
- Matplotlib is platform-independent so it can run on various operating systems, including Windows, macOS, and Linux.
- Through the use of widgets and event handling one can create interactive plots with Matplotlib.
Seaborn
- Seaborn is a data visualization library that provides a high-level interface for drawing attractive and informative graphics for machine learning
- It is built on Matplotlib and is closely integrated into the data structures of Pandas
- Several built-in themes and color palettes improve the aesthetics of plots in Seaborn
- Seaborn simplifies the creation of complex plots with its high-level functions
XGBoost
- XGBoost is developed to optimize speed and performance in machine learning tasks
- Some noted features of XGBoost are gradient boosting framework, regularization and parallel processing.
- While XGBoost is a sequential learning algorithm, to enhance the system efficiency it can also incorporate parallel computation
- Some tree and linear model algorithms that XGBoost supports are Exact Greedy Algorithm, Approximate Algorithm and Histogram-Based Algorithm:
Also read: Flask vs. Django: A Comparison of Python Web Frameworks
Comprehensive Guidance of Python Libraries With Interview Kickstart
Interview Kickstart, a global leader in career uplevelling, offers a lot of AI/ML courses in which knowledge of Python and its libraries is being given to candidates.
Topics covered include coding techniques, file and exception handling, visualization with Python and a comprehensive understanding of libraries like Numpy, Pandas, etc.
One can learn Python as part of the comprehensive machine learning course offered by Interview Kickstart.
Other courses such as Data Science, Applied Gen AI and Advanced Gen AI also provide comprehensive knowledge of Python and its machine learning libraries.
The courses also prepare the candidates to excel in interviews at FAANG+ companies making them ideal tech interview prep courses for AI/ML career aspirants.
There are numerous success stories of candidates who have excelled in interviews with top-tier companies after pursuing one of IK’s courses.
FAQs: Python Machine Learning Libraries
Is PyTorch Better Than TensorFlow?
PyTorch offers flexibility, experimentation and quick editing capabilities making it ideal for research and small-scale projects On the other hand, TensorFlow is best suited for large-scale projects that require high-performance serving capabilities for scalable solutions.
Which Is the Best Python Machine Learning Library?
Scikit Learn is considered to be the best Python machine learning library. Applications in which it can be used are classification, regression, clustering, model selection, naive Bayes’, K-means, preprocessing and supervised learning to name a few.
Is it Necessary to Learn Every Library of Python Before Proceeding to Machine Learning?
You just learn the basics of the Python programming language and a few most prominently used Python machine learning libraries to enter the domain of Machine learning.
What Is the Most Beginner Friendly Python Machine Learning Library?
According to many researchers, Pytorch is considered to be the most beginner-friendly machine learning library. There is no need for high-level APIs like in many other libraries to get started. Secondly, transition to complex models can be easily performed with Pytorch.
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