Jupyter
An open-source platform that allows users to create and share documents including live code, data visualization, equation, etc. Besides this, it offers rapid prototyping, and works with different kernels, and supports almost 40 languages. Jupyter contains modules for both coding and presentational elements (such as images or calculations).
Read this article to know more about this: How To Use Jupyter Notebook – An Ultimate Guide
Reason to Pick Jupyter
Below are some of the major highlights to justify the same:
- It offers immediate output to users and working on this tool is highly flexible for developers
- Jupyter is the best pick in IDE for machine learning for data cleaning and transformation, scientific calculation, statistical modeling, and much more.
- It is backed by programming languages such as Julia, Python, and R, and offers a highly interactive environment that allows developers to combine code, images, plots, etc.
Best IDEs For Machine Learning
Over the years, Machine Learning has made a significant impact in the market. As per a recent report, currently, the market is standing at USD 21 Billion (2022) and is expected to cross USD 200 Billion by the end of 2029 at a CAGR of 38% and above. This graph has marked the influential leap during the COVID-19 pandemic and the demand rose by multiple folds (specifically for ML & AI) and the market is not showing any sign of slowing down.
Since the demand for Machine Learning and its professionals is increasing every day, it becomes crucial for you to understand that it’s not as easy as it seems. Being one of the most fascinating technology jobs, the key to getting sustainable growth in this field is to keep yourself updated with the latest tools and techniques to get ahead in this field.
Before we jump into the mainframe, let’s start with the basics of Machine Learning along with its scope in the future: