This article is part of our series on "AI education" (In partnership with Paperspace) In the past few years, Python has become the preferred programming language for machine learning and deep learning. Most books and online courses on machine learning and deep learning either feature Python exclusively or along with R. Python has become very popular because of its rich roster of machine learning and deep learning libraries, optimized implementation, scalability, and versatile features. But Python is not the only option for programming machine learning applications. There's a growing community of developers who are using JavaScript to run machine learning models. While JavaScript is not a replacement for the rich Python machine learning landscape (yet), there are several good reasons to have JavaScript machine learning skills.

If you're starting a new machine learning or deep learning project, you may be confused about which framework to choose. As we'll discuss, there are several good options for both kinds of projects. There is a difference between a machine learning framework and a deep learning framework. Essentially, a machine learning framework covers a variety of learning methods for classification, regression, clustering, anomaly detection, and data preparation, and may or may not include neural network methods. A deep learning or deep neural network framework covers a variety of neural network topologies with many hidden layers.

If you're starting a new machine learning or deep learning project, you may be confused about which framework to choose. As we'll discuss, there are several good options for both kinds of projects. There is a difference between a machine learning framework and a deep learning framework. Essentially, a machine learning framework covers a variety of learning methods for classification, regression, clustering, anomaly detection, and data preparation, and may or may not include neural network methods. A deep learning or deep neural network framework covers a variety of neural network topologies with many hidden layers.

Raw data in its unprocessed state does not offer much value, but with the right analytics techniques can offer rich insights that can aid various aspects of life such as making business decisions, political campaigns, and advancing medical science. As shown in Figure 1, the analytics cycle can be broadly classified into four categories or phases: descriptive, diagnostic, predictive and prescriptive. Machine Learning is an approach to data analysis that automates analytical model building and is used in all four types of analytics. The relevance and the growing use of analytics using machine learning can be demonstrated by its widespread use in the 2016 US presidential election campaign. Unprecedented growth in the availability of useful information coupled with advancements in technology are making it attractive to use analytics to build and run a better campaign.