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The data sets are categorized according to varying difficulty levels to be suitable for everyone. Applications of NaturalLanguageProcessing One of the essential things in the life of a human being is communication. This blog will discuss the different naturallanguageprocessing applications.
The data sets are categorized according to varying difficulty levels to be suitable for everyone. Link to blog -> Fine-tune LLMs Applications of NaturalLanguageProcessing One of the essential things in the life of a human being is communication.
Dealing with large datasets: With the exponential growth of data in various industries, the ability to handle and extract insights from large datasets has become crucial. Data science equips you with the tools and techniques to manage big data, perform exploratorydataanalysis, and extract meaningful information from complex datasets.
Prescriptive Analytics Projects: Prescriptive analytics takes predictive analysis a step further by recommending actions to optimize future outcomes. NLP techniques help extract insights, sentiment analysis, and topic modeling from text data. 6. Analyzing Large Datasets: Choose a large dataset from public sources (e.g.,
R’s machine learning capabilities allow for model training, evaluation, and deployment. · Text Mining and NaturalLanguageProcessing (NLP): R offers packages such as tm, quanteda, and text2vec that facilitate text mining and NLP tasks.
D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning. DataWrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.
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