Key Python Libraries For Machine Learning Models in 2024

The reason why Python is extensively used and considered one of the top-used programming languages in 2024 is its ease of use, clean and readable syntax, extensive 3rd party integration capabilities, and wide range of free libraries.

In fact, the design philosophy of the Python programming language focuses on code readability through exclusive use of whitespace. On the flip side, its object-oriented approach helps developers write clean and logical codes for small, medium, and large projects.

This has made the Python programming language a staple in data science and machine learning allowing developers to use the language to create data visualization, statistical calculations, manipulate and analyze data, build foundational models, and more.

Additionally, Gen AI led the rise in its adoption and scaling, which highly earned the business value for the takes, shapers, and makers who used the AI and Machine Learning models customized with proprietary data or built from scratch.

Even if you haven’t worked closely with ML models, this blog is a detailed guide that will save your time in exploring and selecting the best Python libraries for Machine Learning projects. It also covers answers to why decision makers like you choose Python as backend technology over others.

Top 5 Python Libraries For Machine Learning in 2024

Being one of the trusted Python development companies, TOPS has explicitly researched and has come up with the list of best Python libraries for Machine Learning after considering its usage, use cases, and popularity. Scroll down to explore the capabilities and implementation of these Python libraries for ML models.

1. TensorFlow Python

Python’s Machine Learning Library built by Google, TensorFlow Python is popular for what’s called ‘differentiable programming’. It automatically computes a specific function’s derivatives with the programming language. With TensorFlow, users can easily evaluate and develop Machine Learning and Deep Learning models through its flexible framework and architecture. It’s easy to leverage TensorFlow Python to visualize ML models on both mobile and desktop.

Reasons for TensorFlow’s Popularity

• Building, training, and deploying ML models.
• Makes simple data graphs out of complex computations.
• Flexibility with deep and recurrent neural networks.

Capabilities of TensorFlow

• Users can divide work into multiple GPUs or CPUs, allowing developers to benefit from parallel computing.
• Easy to build neural networks as it offers functions to activate, built layers, and loss functions.
• Interactive visualization of model graphs and training metrics helps in model visualization through the use of TensorBoard.
• Seamless deployment of TensorFlow models on environments like Cloud, web, and mobile for easy scalability.

Use Cases of TensorFlow

• Object recognition, facial recognition, medical imaging, and quality analysis in manufacturing.
• Language translation, text classification, and sentiment analysis.
• Speech and translation recognition.
• Unique recommendation systems.

Apps Built Using TensorFlow

• Google Photos
• Google Voice Search

2. PyTorch

PyTorch is highly popular for its use in large and dense data sets or graphs. It is an open-source ML Python library that is based on Torch, a C programming language framework. This Python library is used in Machine Learning applications, especially for natural language processing and Computer Vision. PyTorch is a deep-learning Python library built by the Facebook AI research team.

Capabilities of PyTorch

• Helps users execute multi-dimensional operations and supports broadcasting and slicing.
• Extended data sets are possible for scalable Deep Learning models. You can leverage efficient distributed computing that offers distributed communication, optimization, and data loading.
• It offers tools for seamless deployment and integration into development environments.

Use Cases of PyTorch

• Solve tasks related to gaming features, decision making, and robotics that are controlled through reinforcement learning libraries.
• Leverage pre-trained models like GPT, BERT, or ResNet to streamline particular tasks.

Apps Built Using PyTorch

• Samsung Electronics
• Apple

3. Keras

Designed specifically for building neural networks for Machine Learning models, Keras becomes fully functional to train neural networks with minimal coding. It builds and evaluates neural networks with ML and Deep Learning models.

Keras is Popular For

• Works across multiple environments with flexibility and portability.
• It is extensible in nature.
• Easy integration with activation functions, optimizers, layers, and objectives.
• Mostly used for agile prototyping.

Capabilities of Keras

• Offers easy functions and tools for data augmentation techniques.
• You can build your own neural network model through custom activation and loss functions.
• Seamless evaluation of trained models and offers required tools for calculating precision, F1 score, and recall.
• Helps users leverage the potential of GPU for boosting inference and training resulting in GPU acceleration as well as distributed computing.

Use Cases of Keras

• Streamlines generating creative content, data augmentation, and image synthesis tasks.
• Simplified speech recognition.
• Speedy object detection.
• Smooth image classification such as identification of objects from images.

Apps Built Using Keras

• Netflix
• Uber

4. Scikit-Learn

Scikit-Learn’s simplified design makes it a user-friendly library for those new to ML models. It ensures engaging UI Machine Learning tasks. It is unique among the popular Machine Learning libraries of Python. It makes it easy for classification, clustering, dimensionality reduction, regression, and model selection.

Capabilities of Scikit-Learn

• Smooth functionalities for handling data, managing missing values, feature extraction techniques, and encoding categorical variables.
• Allows searching structures, relationships, and patterns with unlabeled data.
• To make predictions, it helps models learn from variable data sets.

Use Cases of Scikit-Learn

• Allows predicting class labels through algorithms such as random forests or gradient boosting methods.
• Implementation of decision tree regression, linear regression, and polynomial regression.
• Helps leverage clustering algorithms to discover patterns and for seamless classification of identical data points without class labels.

Apps Built Using Scikit-Learn

• Inria
• Spotify

5. Matplotlib

It is a Python library specifically developed to visualize data through tools that build static, interactive, and animated visualizations. This involves creating beautiful graphs, bar charts, plots, histograms, and more. This is popularly known for data visualization patterns. It has modules that make plotting easier through tools for controlling font properties, line styles, and more.

Capabilities of Matplotlib

• Helps showcase different bar sizes for data comparison.
• Swift visualization of the relationship between two attributes to explore the trends, changes, and patterns.
• Seamlessly add text, labels, or annotations.
• Create pie charts to represent % as a whole with effective personalization in labels and colors.
• Data visualization in a three-dimensional landscape.

Use Cases of Matplotlib

• Histograms
• Geographic maps
• Heatmaps

In order to select the right Python libraries for your Machine Learning project, you need an expert consultation that helps drive your software development project in the right direction using the latest tools and agile methodologies. And TOPS has rich experience working on complex Python projects, offering new-age Python development services.

We have solved some of the unique business challenges by building next-gen Python apps

• Our Python team has built an Infrastructure as a Code (IaaC) platform where the users can register on the platform, choose the plan, and provide Amazon marketplace details. Once users add the details, the platform generates AWS infrastructure in the backend for the user, based on their requirements. We have used Cloud Formation, RDS, Lambda, EC2, SQS, S3 from AWS and Boto3 to access AWS via Python code.
• An app that controls product reviews with automated discovery of negative /positive reviews through the use of Python libraries like SKLearn, TF-IDF, Pandas, NumPy, Django, and Django Rest Framework (DRF).
• By using the Azure AI, Django, and Django Rest Framework, we generated an OCR model that fetched data from the parking fines receipts.
• Built a solution where users can upload their documents like PDF, Doc file, and other formats. The ML models fine-tune the text inside the document and users can ask questions that the model answers based on the fine-tuned text. We have used FastAPI, Transformers, Watchdog, Cryptography, LlamaIndex, Gradio, PyTorch, and Sentence-Transformers for this project.

Explore how TOPS Infosolutions can be your extended tech team to handle any technology complexities for your software development needs.

Why TOPS as Your Trusted Software Development Partner?

We have a rock solid project management process in place to meet our client’s needs, minimize project risks, and avoid budget overruns. What’s most significant is our consistency in staying abreast with the latest technologies and trends. Hire Python developers who have gained tech excellence and worked on time-tested development processes for optimum outputs.