18 best Python Machine learning libraries in 2024

Python Machine Learning libraries help develop supervised and unsupervised learning, data pre-processing, feature extraction tools, and deep learning. 

Following are the top use cases of these shortlisted libraries for Python Machine Learning,

• Pre-processing of data that includes data cleaning and feature engineering tasks such as normalization, imputation, missing value treatment, and outlier detection.
• Model selecting and optimizing, such as cross-validation, hyperparameter tuning, and model selection metrics.
• Visualizations to understand data and results. This includes visualizing data distributions, feature importance, and model performance.
• Developing algorithms, including supervised learning algorithms (e.g. linear regression, logistic regression, support vector machines, decision trees, random forests, and neural networks) as well as unsupervised learning algorithms (e.g. clustering, dimensionality reduction, and anomaly detection).
• Calculating performance metrics such as accuracy, precision, recall, and F1 score.

The following is a list of the 18 most popular open-source Python libraries for Machine Learning,

keras:  

• It provides a high-level API for building and training deep neural networks.   
• Keras allows you to define and incorporate custom layers and loss functions.    
• Configure Keras to run on top of deep learning frameworks like TensorFlow, etc.

Scikit-Learn:  

• It is an essential library in the field of machine learning and data science.  
• It provides tools for cross-validation, hyperparameter tuning, and model selection.    
• The library runs on top of other scientific Python libraries like NumPy and SciPy.   

Pandas:  

• It is a popular Python library for data manipulation and analysis.   
• It offers tools for data cleaning. This includes handling missing values, data alignment, and data type conversion.   
• It supports time series data, making it valuable for financial analysis and forecasting.   

YOLOv5:  

• "You Only Look Once version 5," is a popular computer vision model for object detection. 
• It is popular for its real-time object detection capabilities. 
• It has improved upon the accuracy of its predecessors while maintaining its speed. 

Ray:  

• It is an open-source distributed computing framework used in Python.   
• It enables you to parallelize and distribute Python applications.   
• It helps with low-latency, high-throughput computing tasks.   

ML-From-Scratch:  

• This helps you gain a deep understanding of the underlying algorithms and mathematics.   
• This allows you to customize it for your specific problem and data. This makes it more effective and efficient.   
• Building models from scratch provides insight into optimization techniques.   

examples:  

• It helps in AI, ML, DL, Pytorch, TensorFlow applications.   
• This library in PyTorch is essential for working with computer vision tasks.   
• You can access pre-trained models like ResNet, VGG, and AlexNet through "torchvision.models".   

Paddle:  

• It is an open-source deep learning platform developed by Baidu.  
• It is a powerful deep learning framework, like TensorFlow and PyTorch.   
• It focuses on simplicity and efficiency.  

rasa:  

• It is an open-source Python library designed for building conversational AI apps.   
• It provides tools for creating and managing conversational flows.  
• It supports many languages and can helps in a global context.  

horovod:  

• It is a popular library in Python used for distributed deep learning.   
• It enables you to scale your DL models to many GPUs and even across many machines.   
• It supports various deep learning frameworks like TensorFlow, PyTorch, and MXNet.   

mlflow:  

• It is an open-source platform for managing the end-to-end machine learning lifecycle.  
• It allows you to log and compare experiments.  
• It provides tools for packaging models in a standard format.

imgaug:  

• It is an important tool for image augmentation. It is especially used in machine learning and computer vision tasks.   
• It allows you to customize augmentation pipelines to suit your specific needs.    
• It works well with other popular libraries like OpenCV and NumPy.  

ChatterBot:  

• It provides a framework and pre-built components. That makes it easier to create chatbots.   
• This library often includes NLP capabilities. This allows chatbots to understand and generate human-like text responses.  
• These libraries offer options for customizing the behavior and responses of chatbots.

nni:  

• NNI handles distributed training, making it suitable for large-scale experiments.  
• NNI is important for streamlining and improving the machine learning model development process.  
• It automates and optimizes ML model selection and hyperparameter tuning.    

numpy-ml:  

• It is a fundamental library in the Python ecosystem. It is especially used in the context of machine learning and data science.  
• It is open-source and has a large and active community.  
• It is crucial for performing efficient numerical and array-based operations.  

tpot: 

• It is a Python library for automated machine learning (AutoML).  
• This includes feature selection, data preprocessing, and the choice of models.
• It employs techniques like cross-validation to reduce the risk of overfitting.    

autokeras:  

• It is an open-source library for automated machine learning (AutoML).  
• It simplifies the process of building and training machine learning models.  
• It is accessible to both beginners and experienced ML practitioners.  

pattern:  

• It is often referred to as a design pattern library.   
• It is a collection of reusable solutions to common software design problems.
• These patterns help developers create more efficient, maintainable, and scalable code.  

FAQ 

1. What is scikit-learn?  

It is an ML library for Python. That provides simple and efficient tools for data analysis and modeling. It offers a wide range of algorithms for classification, regression, clustering, and more.    

2. What is PyTorch?   

PyTorch is an open-source machine learning library. It is developed by Facebook's AI Research lab. It helps with deep learning and provides dynamic computation graphs. This makies it popular among researchers.    

3. What is Keras?   

Keras is an open-source deep learning API. That runs on top of other deep learning frameworks like TensorFlow and Theano. It's designed to be and allows for rapid prototyping of neural networks.   

4. How do I install these libraries?  

You can install these libraries using Python's package manager, pip. For example, you can install scikit-learn with pip install scikit-learn. Also, install TensorFlow with pip install tensorflow, and PyTorch with pip install torch.    

5. What is the difference between a tensor and an array in TensorFlow?  

In TensorFlow, a tensor is a multi-dimensional array. This array can be placed on GPU for accelerated computation. It is like NumPy arrays but optimized for deep learning tasks.