Machine learning (ML) is arguably the most sought after skill in the fields of data and computer science. ML related projects and problems can be done using any programming language.
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages— Python, R and Java often top the list.
Python is currently the most commonly used programming language in data science, due to its flexibility and an abundance of out-the-box libraries to shorten development time.
The consensus among developers is that JavaScript is primarily a language for frontend development. In the context of machine learning, most assumed JavaScript only had applications in data visualization, but since the creation of Node.js and other powerful libraries, almost anything is possible in JavaScript—including machine learning modeling.
With the release of TensorFlow.js in 2018, JavaScript presently equipped for bringing Machine learning to JavaScript developers — both in the browser and server-side. The adoption of TensorFlow.js for structured data has been challenging, due to the iterative processes involved in the ML workflow.
Here is the interesting part: The most used machine learning tool for structured data preprocessing is Pandas, which is a library written in Python. But the good news for JavaScript developers is the emergence of a new library called Danfo.js, which is written in JavaScript. Danfo.js is designed to mimic the robust functionality of Pandas when it comes to structured data preprocessing. In this series, we’ll learn how Danfo.js stacks up for JavaScript developers.
What you will learn in this series
- Danfo.js library (part 1)
- Observable notebook overview (part 1)
- Getting started with Danfo.js in an Observable notebook
- Exploratory Data Analysis with danfo.js (part 1)
- Data preprocessing for modeling (part 2)
- Model building and…
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