Nikhil Abraham

Nikhil Abraham was Director of Business Development & Growth at Codecademy. In that role, he taught and trained thousands of beginning coders across a variety of professions. He helped refine Codecademy's online courses, which have introduced basic coding skills to millions of learners.

Articles & Books From Nikhil Abraham

Article / Updated 07-17-2017
You will be able to code and complete some small apps by yourself, but the apps you build at work or use every day, like Google Maps or Instagram, are created by teams of people. Teams for a single product can vary in size, reaching to upward of 50 people, and each person plays a specific role across areas like design, development, product management, and testing.
Article / Updated 07-17-2017
Once you know what your app will do, you can focus on how your app will do it. After breaking down your app into steps, you go over each step to determine how to accomplish it. For more complicated apps, developers first decide which of these two methods is the best way to complete each step: Building code from scratch: This is the best option if the functionality in a particular step is unique or strategically important, an area of strength for the app, and existing solutions are expensive or nonexistent.
Article / Updated 07-27-2022
There are many tools available to help coders do their best work. Before you start coding, do a few housekeeping items. First, ensure that you are doing all of the following: Using the Chrome browser: Download and install the latest version of Chrome, as it offers the most support for the latest HTML standards.
Article / Updated 07-27-2022
Dates can present problems in data. For one thing, dates are stored as numeric values. However, the precise value of the number depends on the representation for the particular platform and could even depend on the users’ preferences. For example, Excel users can choose to start dates in 1900 or 1904. The numeric encoding for each is different, so the same date can have two numeric values depending on the starting date.
Article / Updated 07-17-2017
Rating data has its limitations in machine learning. For recommender systems to work well, they need to know about you as well as other people, both like you and different from you. Acquiring rating data allows a recommender system to learn from the experiences of multiple customers. Rating data could derive from a judgment (such as rating a product using stars or numbers) or a fact (a binary 1/0 that simply states that you bought the product, saw a movie, or stopped browsing at a certain web page).
Article / Updated 07-17-2017
special formatting, you do have to consider how the text is stored and whether it contains special words within it. The multiple forms of encoding present on web pages can present interpretation problems that you need to consider as you work through the text.For example, the way the text is encoded can differ because of different operating systems, languages, and geographical areas.
Article / Updated 07-17-2017
Both averaging and voting systems can also work fine when you use a mix of different machine learning algorithms. This is the averaging approach, and it’s widely used when you can’t reduce the estimate variance.As you try to learn from data, you have to try different solutions, thus modeling your data using different machine learning solutions.
Article / Updated 07-17-2017
As human beings, understanding language is one of our first achievements, and associating words to their meaning seems natural. It’s also automatic to handle discourses that are ambiguous, unclear, or simply have a strong reference to the context of where we live or work (such as dialect, jargon, or terms family or associates understand).
Article / Updated 07-17-2017
Jobs in coding involve more than just sitting in front of a computer. There are other steps you must take in order to ensure that a project is seen through to the end. The purpose of the planning phase is to Understand the client goals. Some clients may want to be the first to enter an industry with an app, even if it means sacrificing quality.
Article / Updated 07-17-2017
Given the neural network architecture, you can imagine how easily the algorithm could learn almost anything from data, especially if you added too many layers. In fact, the algorithm does so well that its predictions are often affected by a high estimate variance called overfitting. Overfitting causes the neural network to learn every detail of the training examples, which makes it possible to replicate them in the prediction phase.