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They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. From automating tedious tasks to unlocking insights from unstructureddata, the potential seems limitless.
Machine Learning is the method of teaching computer programs to do a specific task accurately (essentially a prediction) by training a predictivemodel using various statistical algorithms leveraging data. Introduction Let’s have a simple overview of what Machine Learning is. Source: [link] For […].
Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructureddata for various academic and business applications.
For example, knowledge graphs can be used to provide structureddata to train LLM, and LLM can be used to extract information from unstructureddata sources such as text and images, which can then be incorporated into knowledge graphs. Knowledge graphs will continue to be essential for AI in the era of ChatGPT and LLM.
Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictivemodel from the training inputs. Popular algorithms for topic modeling include Latent Dirichlet Allocation (LDA) and non-negative matrix factorization (NMF).
Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.
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