Natural language processing (NLP) is a rapidly growing field of computer science that uses machine learning and deep learning to process large amounts of text data. With the help of TensorFlow, developers have been able to build powerful NLP projects that can be used for various applications such as sentiment analysis, text classification, question answering systems and so on. Sentiment Analysis: Sentiment analysis is one of the most popular application areas for NLP using TensorFlow. It involves understanding natural language in terms of its underlying emotions or attitudes expressed by people through their words or expressions. By leveraging advances in deep learning technology like convolutional neural networks (CNNs) along with word embeddings from pre-trained models like GloVe and Word2Vec, developers are able to accurately classify texts according to its sentiment polarity into positive/negative classes without having any prior knowledge about the domain it belongs too .

There is a lot of excitement around AI. Recently there has been an incredible amount of buzz around the demos of models like ChatGPT and Dall-E-2. As impressive as these systems are, I think it becomes increasingly important to keep a level head, and not get carried away in a sea of excitement. The following videos/lectures are more focused on how to think about data science projects, and how to attack a problem. I've found these lectures to be highly impactful in my career and enabled me to build effective and practical solutions that fit the exact needs of the companies I've worked for.

You can extract desired topics from large volumes of text using the applications of Natural Language Processing (NLP). The large text can include customer reviews of movies, restaurants, etc., feeds from social media, emails of customer complaints, user feedback, and so on.

Natural Language Processing is one of the two big subfields in Machine Learning. In the 2020s, Natural Language Processing will be one of the biggest things to know for business. There is so much unstructured text data out there. The people who figure out how to turn that text data into actionable insights will be both rich and influential. You're here because you want to do machine learning.

Before getting into the topic, why is it important to have an NLP project in your portfolio? How can it help in your career? The amount of text data getting generated is growing faster than ever. As per IDC, about 80% of global data will be unstructured by 2025. And this will be the pattern across the industries like retail, technology, healthcare, and anything you name it.

Natural Language Processing, also known as NLP, is a subfield of computer science, specifically artificial intelligence, that focuses on understanding written and spoken text. It covers various tasks some of which are speech recognition, sentiment analysis and language generation; And, it has been applied in several use cases such as machine translation, spam detection, virtual assistants and chatbots. The project covered in this article is a sentiment analysis project called Natural Language Processing with Disaster Tweets. Sentiment analysis is the process to extract subjective qualities from text such as emotion or attitude. The objective of the project is to identify if a specific tweet is a real disaster or not. The project is ideal for beginners in NLP.

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that is concerned with the interactions made between computers and natural language. Essentially, by analyzing and representing natural language computationally, computers are capable of understanding natural language and responding in a way similar to a human. As a beginner learning the ropes of any new technology, getting your hands dirty is an important part of the learning process. Although I believe theoretical knowledge is very crucial, I don't believe it's effective in isolation as the theory doesn't always translate into real-world scenarios. Taking a practical approach is by far the greatest way to keep testing yourself whilst gaining experience of what it's like to work in a real-world environment.

An effective chatbot requires a massive amount of training data in order to quickly solve user inquiries without human intervention. However, the primary bottleneck in chatbot development is obtaining realistic, task-oriented dialog data to train these machine learning-based systems. We've put together the ultimate list of the best conversational datasets to train a chatbot, broken down into question-answer data, customer support data, dialogue data and multilingual data. Question-Answer Dataset: This corpus includes Wikipedia articles, manually-generated factoid questions from them, and manually-generated answers to these questions, for use in academic research. The WikiQA Corpus: A publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering.

Summary: Since BERT NLP models were first introduced by Google in 2018 they have become the go-to choice. New evidence however shows that LSTM models may widely outperform BERT meaning you may need to evaluate both approaches for your NLP project. Over the last year or two, if you needed to bring in an NLP project quickly and with SOTA (state of the art) performance, increasingly you reached for a pretrained BERT module as the starting point. Recently however there is growing evidence that BERT may not always give the best performance. In their recently released arXiv paper, Victor Makarenkov and Lior Rokach of Ben-Gurion University share the results of their controlled experiment contrasting transfer-based BERT models with from scratch LSTM models.

When we are working on any NLP project or competition, we spend most of our time on preprocessing the text such as removing digits, punctuations, stopwords, whitespaces, etc and sometimes visualization too. After experimenting TextHero on a couple of NLP datasets I found this library to be extremely useful for preprocessing and visualization. This will save us some time writing custom functions. We will apply techniques that we are going to learn in this article to Kaggle's Spooky Author Identification dataset. You can find the dataset here.