Natural Language Processing, popularly known in its abbreviated form NLP, is a branch of Artificial Intelligence that deals with the interaction between humans and computers using the natural language. Its ultimate objective is to meet, understand, and make sense of the human language. NLP is that branch of AI that helps computers to interpret, understand and manipulate the human language. NLP helps developers to structure and organize information to perform tasks such as speech recognition, translation, summarization, relationship extraction, etc. NLP is a way for computers to understand, analyze, and derive meaning from human languages.
Great Learning brings you this beginner-friendly tutorial on Natural Language Processing to help you understand everything you need to know about this topic and getting started on the journey to learn about it well. This video starts with an introduction to NLP, followed by understanding topics such as tokenization, normalization and stemming. Then we look at sentence segmentation and phrase identification. Following this, we will understand the topic of word sense disambiguation. Finally, we look at parsing! This video teaches NLP and its key functions and concepts with a variety of demonstrations & examples to help you get started on the right foot.
Teaching machines to understand human context can be a daunting task. With the current evolving landscape, Natural Language Processing (NLP) has turned out to be an extraordinary breakthrough with its advancements in semantic and linguistic knowledge. NLP is vastly leveraged by businesses to build customised chatbots and voice assistants using its optical character and speed recognition techniques along with text simplification.
To address the current requirements of NLP, there are many open-source NLP tools, which are free and flexible enough for developers to customise it according to their needs. Not only these tools will help businesses analyse the required information from the unstructured text but also help in dealing with text analysis problems like classification, word ambiguity, sentiment analysis etc.
Here are eight NLP toolkits, in no particular order, that can help any enthusiast start their journey with Natural language Processing.
Also Read: Deep Learning-Based Text Analysis Tools NLP Enthusiasts Can Use To Parse Text
About: Natural Language Toolkit aka NLTK is an open-source platform primarily used for Python programming which analyses human language. The platform has been trained on more than 50 corpora and lexical resources, including multilingual WordNet. Along with that, NLTK also includes many text processing libraries which can be used for text classification tokenisation, parsing, and semantic reasoning, to name a few. The platform is vastly used by students, linguists, educators as well as researchers to analyse text and make meaning out of it.
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This video will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and more. Python, NLTK, & Jupyter Notebook are used to demonstrate the concepts.
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=X2vAabgKiuM&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=16
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It’s 2021, everything is getting replaced by a technologically emerged ecosystem, and mobile apps are one of the best examples to convey this message.
Though bypassing times, the development structure of mobile app has also been changed, but if you still follow the same process to create a mobile app for your business, then you are losing a ton of opportunities by not giving top-notch mobile experience to your users, which your competitors are doing.
You are about to lose potential existing customers you have, so what’s the ideal solution to build a successful mobile app in 2021?
This article will discuss how to build a mobile app in 2021 to help out many small businesses, startups & entrepreneurs by simplifying the mobile app development process for their business.
The first thing is to EVALUATE your mobile app IDEA means how your mobile app will change your target audience’s life and why your mobile app only can be the solution to their problem.
Now you have proposed a solution to a specific audience group, now start to think about the mobile app functionalities, the features would be in it, and simple to understand user interface with impressive UI designs.
From designing to development, everything is covered at this point; now, focus on a prelaunch marketing plan to create hype for your mobile app’s targeted audience, which will help you score initial downloads.
Boom, you are about to cross a particular download to generate a specific revenue through your mobile app.
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Working with natural language data can often be challenging due to its lack of structure. Most data scientists, analysts and product managers are familiar with structured tables, consisting of rows and columns, but less familiar with unstructured documents, consisting of sentences and words. For this reason, knowing how to approach a natural language dataset can be quite challenging. In this post I want to demonstrate how you can use the awesome Python packages, spaCy and Pandas, to structure natural language and extract interesting insights quickly.
spaCy is a very popular Python package for advanced NLP — I have a beginner friendly introduction to NLP with SpaCy here. spaCy is the perfect toolkit for applied data scientists when working on NLP projects. The api is very intuitive, the package is blazing fast and it is very well documented. It’s probably fair to say that it is the best general purpose package for NLP available. Before diving into structuring NLP data, it is useful to get familiar with the basics of the spaCy library and api.
After installing the package, you can load a model (in this case I am loading the simple Engilsh model, which is optimized for efficiency rather than accuracy) — i.e. the underlying neural network has fewer parameters.
import spacy nlp = spacy.load("en_core_web_sm")
We instantiate this model as nlp by convention. Throughout this post I’ll work with this dataset of famous motivational quotes. Let’s apply the nlp model to a single quote from the data and store it in a variable.
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A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.
This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!
In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2
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