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How to get datasets for Machine Learning?

Knowledge Hut

Datasets are the repository of information that is required to solve a particular type of problem. Also called data storage areas , they help users to understand the essential insights about the information they represent. Datasets play a crucial role and are at the heart of all Machine Learning models.

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A Day in the Life of a Data Scientist

Knowledge Hut

Join me on this captivating expedition as we peel back the curtain, revealing the intricacies that define "A Day in the Life of a Data Scientist." This blog offers an exclusive glimpse into the daily rituals, challenges, and moments of triumph that punctuate the professional journey of a data scientist.

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Tips to Build a Robust Data Lake Infrastructure

DareData

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data.

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Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. A pipeline may include filtering, normalizing, and data consolidation to provide desired data.

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15 Top Machine Learning Projects for Final Year Students

ProjectPro

Datasets like Google Local, Amazon product reviews, MovieLens, Goodreads, NES, Librarything are preferable for creating recommendation engines using machine learning models. They have a well-researched collection of data such as ratings, reviews, timestamps, price, category information, customer likes, and dislikes.

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Data Engineer vs Data Scientist- The Differences You Must Know

ProjectPro

This blog on Data Science vs. Data Engineering presents a detailed comparison between the two domains. Data Science- Definition Data Science is an interdisciplinary branch encompassing data engineering and many other fields. It entails generating data visualizations and charts for analysis.

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How to Become a Data Engineer in 2024?

Knowledge Hut

If we look at history, the data that was generated earlier was primarily structured and small in its outlook. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets. However, as we progressed, data became complicated, more unstructured, or, in most cases, semi-structured.