Machine Learning Libraries
We delve into the realm of machine learning libraries, exploring the diverse array of tools available to data scientists and machine learning practitioners. From TensorFlow, an open-source framework for building and training deep learning models, to Scikit-learn, a comprehensive toolkit for traditional machine learning algorithms, these libraries offer the flexibility and scalability needed to tackle a wide range of machine learning tasks.
10. Hugging Face
A one-stop shop for open-source machine learning development is now The Hugging Face. It’s convenient to instruct, assess, and implement your models utilizing different Hugging Face ecosystem technologies since it offers simple access to datasets, cutting-edge models, and inference. Additionally, it enables access to high-end GPUs and enterprise solutions. This is the only platform you need, whether you are a professional, researcher, or student studying machine learning, to create excellent solutions for your assignments.
You can refer to our existing article – Hugging Face Transformers Introduction
11. TensorFlow
It is an open-source machine learning framework and it is used for building and training machine learning models, especially deep learning models. TensorFlow gives a comprehensive tools and libraries for various numerical computations and machine learning, makes it suitable for range of applications.
You can refer to our existing article – Introduction to TensorFlow
12. Scikit-learn
Scikit-learn offers functions for selecting and evaluating models, fitting models, and preparing and transforming data. Building on the foundation of the scientific computing libraries SciPy and NumPy as well as Matplotlib for data visualization, Scikit-learn is an open-source machine learning toolkit for Python. In the jargon of sci-kit-learn, it supports machine learning with and without supervision and comes with a variety of models and techniques known as estimators.
The library, which was formerly known as scikits. learn, was created as a Google Summer of Code project in 2007 and saw its first public release in 2010. Other SciPy add-on packages also utilize the first part of its name, which is short for SciPy toolkit. Numerical data saved in NumPy arrays or SciPy sparse matrices is the main type of data that Scikit-learn processes.
You can refer to our existing article – Learning Model Building in Scikit-learn
Top 20 Data Science Tools in 2024
Enterprise data is growing more and more challenging, and because it plays a critical role in strategic planning and decision-making, organizations are being pushed to spend on the people, procedures, and technology necessary to extract useful business insights from their data assets. As we delve into 2024, the landscape of data science tools has been remarkable innovations and remarkable.
This blog will look at the Top 20 data science tools for 2024. These technical improvements make the ingestion, cleansing, processing, analysis, modeling, and display of data easier. Also, certain technologies provide machine learning ecosystems for the building, tracking, deployment, and monitoring of models.
Table of Content
- What are Data Science tools?
- Why do we need Data Science Tools?
- Top 20 Data Science Tools
- Popular Languages
- Python-based data analysis tools
- Open-Source Data Science Tools
- Big Data Processing Tools
- Machine Learning Libraries
- Tools for Managing Databases
- Data Visualiztaions & Buisness Intelligence(BI) Tools
- Statistical Analysis Tools
- Conclusion
Contact Us