Deep Learning, NLP, NMT, AI, ML
Now, we can see that our target has changed to 0 and 1,i.e. 0 for Negative and 1 for Positive, and the data is more or less in a balanced state. Data Pre-processing Now, we will perform some pre-processing on the data before converting it into vectors and passing it to the machine learning model. We will create a function for pre-processing of data. 1. First, we will iterate through each record, and using a regular expression, we will get […]
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This article was published as a part of the Data Science Blogathon Introduction In this tutorial, I am going to discuss a practical guide of Natural Language Processing(NLP) using Python. Before we move further, we will just take a look at the concept of Corona Virus namely CoVid-19. CoVid-19: Coronavirus disease (CoVid-19) is an infectious disease that is caused by a newly discovered coronavirus. Most of the people who have been infected with the CoVid-19 virus will experience mild to adequate […]
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This article was published as a part of the Data Science Blogathon Image by Author made online on befunky.com Intro: According to experts, 80% of the world’s existing data is in the form of unstructured data(images, videos, text, etc). This data could be generated by Social media tweets/posts, call transcripts, survey or interview reviews, text across blogs, forums, news, etc. It is humanly impossible to read all the text across the web and find patterns. Yet, there is definitely […]
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“The quality of your communication shapes the quality of your life.”, with this beautiful line let’s s begin and understand what we will learn in this article. In my one of the article, I have explained how to automate machine learning problem statement using EvalML. In this article we will look at “is it possible to automate NLP task using EvalML?”. What is EvalML? It is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective […]
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Introduction Every Machine Learning enthusiast has a dream of building/working on a cool project, isn’t it? Mere understandings of the theory aren’t enough, you need to work on projects, try to deploy them, and learn from them. Moreover, working on specific domains like NLP gives you wide opportunities and problem statements to explore. Through this article, I wish to introduce you to an amazing project, the Language Detection model using Natural Language Processing. This will take you through a real-world […]
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This article was published as a part of the Data Science Blogathon. What Is Sentiment Analysis? Conclusions are integral to practically all human exercises and are key influencers of our practices. Our convictions and impression of the real world, and the decisions we make, are, to an impressive degree, molded upon how others see and assess the world. Therefore, when we have to settle on a choice, we regularly search out the assessments of others. Opinions and their related concepts […]
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This article was published as a part of the Data Science Blogathon. Introduction Hi folks, I hope you are doing well in these difficult times! We all are going through the unprecedented time of the Corona Virus pandemic. Some people lost their lives, but many of us successfully defeated this new strain i.e. Covid-19. The virus was declared a pandemic by World Health Organization on 11th March 2020. This article will analyze various types of “Tweets” gathered during pandemic times. […]
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This article was published as a part of the Data Science Blogathon. Introduction Coca-Cola and PepsiCo are well-established names in the soft drink industry with both in the fortune 500. The companies that own a wide spectrum of product lines in a highly competitive market have a fierce rivalry with each other and constantly competing for market share in almost all subsequent product verticals. We will analyze the sentiment of customers of these two companies with the help of 5000 […]
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Introduction The world of Natural language processing is recently overtaken by the invention of Transformers. Transformers are entirely indifferent to the conventional sequence-based networks. RNNs are the initial weapon used for sequence-based tasks like text generation, text classification, etc. But with the arrival of LSTM and GRU cells, the issue with capturing long-term dependency in the text got resolved. But learning the model with LSTM cells is a hard task as we cannot make it learn parallelly.
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I have been using graph lab for quite some time now. The first Kaggle competition I used it for was Click Trough Rate (CTR) and I was amazed to see the speed at which it can crunch such big data. Over last few months, I have realised much broader applications of GraphLab. In this article I will take up the text mining capability of GraphLab and solve one of the Kaggle problems. I will be referring to this problem with […]
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