Deep learning has revolutionized the field of artificial intelligence (AI) in recent years, achieving remarkable results in various tasks, including image recognition, natural language processing, and machine translation. This guide offers a comprehensive overview of deep learning, from its fundamental concepts to…
read moreTopic modeling is a type of statistical model used in natural language processing (NLP) to discover the abstract “topics” that occur in a collection of documents. One of the most common techniques for topic modeling is Latent Dirichlet Allocation (LDA). Here’s a…
read moreExample 1: Removing URLs text_with_url = “Check out our website: www.example.com for more information” clean_text = re.sub(r’http\S+’, ”, text_with_url) print(clean_text) text_with_url: Contains the input text with a URL. re.sub(r’http\S+’, ”, text_with_url): This uses the re.sub() function to replace any sequence of non-whitespace…
read moreProblem Statement: You are a data scientist working for a social media analytics company. Your team is tasked with conducting sentiment analysis on a large dataset of social media posts to gauge public sentiment towards a particular product launch. The dataset contains…
read moreText preprocessing is a fundamental step in most natural language processing (NLP) tasks. It involves transforming raw text into a format that is more suitable for the task at hand, whether it’s information retrieval, text classification, sentiment analysis, etc. Here are some…
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