Why is NLP Important for AI Engineers?

NLP, the abbreviated form of Natural language processing, is an area that concerns itself with letting machines understand all kinds of human language. The ultimate aim of this area has been to allow computers to perform different kinds of important human tasks that shall involve interaction between different human languages, as well as direct communication between humans and computers. Not only that, but the NLP also allows improved communication between human to human, or as far as a simple task of processing, text to speech.

Initially, NLP had its roots starting in the linguistics field, but since the 20th century, it has evolved itself with the new advances in the field of statistical analytics. Specifically, it has erupted in the last few years with the discovery of new artificial intelligence tech.

A few examples of NLP include personal assistants such as Alexa in Amazon, Siri in Apple, automated language translation such as google translate, Skype translator, software that enables question answering such as google search engine, and text summarization are a few examples of NLP being implemented in the real world scenario.

1.  What Are the Must-Have Skills of an AI Engineer –

Machine Learning or ML, and Artificial Intelligence or AI are two of the fastest-growing industries amongst all the sectors pervading the market today. Starting from chatbots to automated cars, cell phones, to almost every electronic device, they are everywhere. Hence the specific skills of AI engineers are growing rapidly in the market. Some of these skills include –

1. Programming Languages – An AI engineer must have a great grasp of the program languages such as C++, Java, Python, R, which is extremely important. Not only are they easy to learn, but they also have a wide scope of implementation. As a matter of fact, Python is considered the lingua franca when it comes to machine learning.
2. Techniques of Signal Processing – AI engineers must have a firm grasp of signal processing techniques such as by using signal processing. If there is additional knowledge of advanced algorithms used to process signals, such as Bandlets, Curvelets, Shearlets, and wavelets, it is a bonus.
3. Algorithms and Applied Maths – Not only must an AI engineer be greatly versed in applied maths, but they also have a great knowledge of the theory of algorithms that may further help them in understanding vital subjects such as Summations, Partial Differential Equations, Quadratic Programming, Lagrange, Convex Optimisation, Gradient Descent and so on.

2.  Why AI Engineers Must Have a Deep Knowledge of Machine Learning Skills –

Machine learning engineering has now become a cornerstone when it comes to AI skills, without the help of machine learning, everything starting from the recommendations that the algorithms make in apps such as Netflix, Youtube, Amazon, and so on, to the tech that involves voice and image recognition, to so many of automated systems that we make use of regularly would not be able to work.

The main reason for this can be contributed to the fact that machine learning engineering falls right at the intersection of software engineering and data science technology. When a data scientist tries to analyze the data that he has collected, in order to bring out actionable insights from them, it is the responsibility of a machine learning engineer to design software that shall work on its own to make use of the data to create automated predictive models.

For instance, Machine learning skills enable the AI engineer to work upon the recommendation algorithms that we come across on many platforms such as youtube or Netflix. Machine learning skills may be utilized in order to develop a machine learning pipeline that can further integrate itself into the platform in a way that when the user clicks n the “next” button at the end of a video, they can next watch a video that falls accordingly with their interests.

3.  NLP Is a Part of Machine Learning and There Is An Importance For the Skill To Be Mastered by AI Engineers –

One of the biggest responsibilities of an NLP engineer involves controlling the interactions that occur between a human language along with a computer’s ability to analyze and process neutral data language. It is true that computers are exceptionally good at managing structured datasets. However, they still require human help when it comes to working with human languages with the thousands of languages and dialects found worldwide. However, it is only with the help of NLP that Google and Alexa understand what the human is saying. NLP engineer skills play a vital role when it comes to programming today’s technology and analyzing the data found in natural languages.

4.  What Are Some of the Best Certification Courses That Offer NLP Training –

1. MIT – Professional Certificate Program in Machine Learning & Artificial Intelligence – This program is an extremely beneficial course when it comes to gaining a practical understanding of the techniques and tools that can be used in machine learning and AI applications. This course allows you to learn by doing.

2. Certified AI Engineer – by USAII™ – Designed for the ones who are looking to become AI Engineers and to gain pace in the AI industry with the most powerful certifications that are offered in this industry. Offered by the United States Artificial Intelligence Institute or USAII™, this AI certification has been curated to provide the professionals with the most demanding AI skills. This certification program will help you learn, AI on Cloud, Python, machine learning pipelines, machine learning algorithms, deep learning foundations, Tensorflow, NLP fundamentals, and more.

3. Machine Learning by Standford University (Coursera) – One of the most popular courses in the market, this course has been designed to instill all the necessary skills and tools that are required in order to start a career as an artificial intelligence engineer or a machine learning engineer. Not only is this course great for those who wish to enter the world of AI with knowledge of the most important skills, but it is also great for those who wish to become more experienced and fluent in their current AI engineer career.