Learn to Develop Applications With Natural Language Understanding Capabilities

NLP or natural language processing is a domain within artificial intelligence (AI) with a focus on tokenising data or breaking down human language into fundamental components. NLP allows computers to process our language into voice or text data by combining statistical ML (machine learning) methods with computational linguistics and models of deep learning. Components of speech tagging and lemmatisation enable machines and professionals to get a deeper understanding of all linguistics elements like intent, sentiment or context. 

If you are interested in developing apps through NLP practices, an AI-102 training course is the best move for your career. It will teach you all the steps involved in creating apps through natural language processing functions. Here’s a glimpse of everything you will learn.

How to Develop Apps Using NLP Capabilities:

1. Basic Text Processing:

Understanding which method is the most vital when it comes to text processing is one of the most vital concepts while dealing with PLs (programming languages). You must know how to deal with strings in a programming language like the back of your hand. You should be able to manipulate text both ways, use regular expressions and slice strings, among other roles. These responsibilities are among the top skills that you must master as you work with NLP. 

A corpus is a text piece that is used to build NLP apps. The chances of your corpus reaching you clean and analysis-ready is extremely low. In the real world, data is complicated and AI-102 certified professionals often perform several cleaning tasks to make the text usable.

2. NLTK Library: 

Natural Language Toolkit is among the oldest NLP libraries in the world. However, even though this library was first released two decades ago, it remains one of the leading resources to learn specific fundamentals related to NLP. It contains several industry-leading resources to learn particular fundamentals of NLP. Some of the best resources implemented in the library are:

• Tokenisers that allow you to split your corpus into words or sentences
• Stemmers ranging from simple to highly advanced
• Word lemmatisation
• N-Grams concepts
• Part-of-speech taggers

3. Reading text data:

The large volumes of text-based data that flows on the web have grown exponentially over the last decade. Besides taking data from the world wide web, NLP professionals and data scientists take on several files in multiple formats. When you complete your AI-102 training, you learn how to read this text data across sources. For instance, JSON and CSV files are common text formats that must be ingested before you proceed to develop your app.

4. Word Vectors and Neural Networks:
   A word vector is among the most useful NLP methodologies used today. Word Vectors are also vital for understanding Artificial Neural Network applications in the NLP context. NLP professionals realised a while back that words being represented as one-hot vectors will lead to several limitations and troubles in the domain. This research breakthrough has allowed NLP practitioners to create word representations bringing in meaning and context to mathematical figures.

Understanding and learning most of the Word Vectors is vital for NLP as a sector and to Machine Learning in general. They will help you understand the deeper mechanisms involved in the functioning of Neural Networks, one of the learning machine learning models today. 

5. Recurrent Neural Networks:

The text generation architecture within Neural Networks is quite different from the architecture in text classification or word vectors. Also known as Recurrent Neural Networks, these Neural Networks have several mechanisms ready for updating and storing data, typically sentences and other forms of chained data. 

Learning about recurrent and artificial Neural Networks provides a strong foundation in these model types. Then, you can easily understand why multiple Neural Network architectures are needed and why a standard size of infrastructure isn’t available for natural language processing (NLP) applications.

6. Text Classification:

This helps you in classifying text into various categories through predictive models. Some of the most common models that professionals with AI-102 training use are 

• Tree-based models
• Neural networks
• Naive Bayes Classifiers

Some common applications of text classification in app development are

• Detecting spam
• Categorising and classifying text
• Sentiment analysis

As a part of an AI-102 training course, you will learn all this and more in much more detail. Add a new skill to your resume and enrol in a training course today.