Data Science Blog

FEBRUARY 23, 2023

This characteristic is clearly observed in models in natural language processing (NLP) and computer vision (CV) like in the graphs below. And annotations would be an effective way for exploratory data analysis (EDA) , so I recommend you to immediately start annotating about 10 random samples at any rate.

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Pickl AI

JULY 12, 2024

Deep Learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, Natural Language Processing, and speech recognition. Natural Language Processing (NLP) This is a field of computer science that deals with the interaction between computers and human language.

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Mlearning.ai

MAY 23, 2023

This includes skills in data cleaning, preprocessing, transformation, and exploratory data analysis (EDA). Blind 75 LeetCode Questions - LeetCode Discuss Data Manipulation and Analysis Proficiency in working with data is crucial. Familiarity with libraries like pandas, NumPy, and SQL for data handling is important.

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Dataconomy

JULY 28, 2023

Data preprocessing is a fundamental and essential step in the field of sentiment analysis, a prominent branch of natural language processing (NLP). Noise refers to random errors or irrelevant data points that can adversely affect the modeling process.

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Pickl AI

JULY 20, 2023

5. Text Analytics and Natural Language Processing (NLP) Projects: These projects involve analyzing unstructured text data, such as customer reviews, social media posts, emails, and news articles. To ascertain the general sentiment and deal with any potential problems, use natural language processing (NLP) tools.

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Pickl AI

SEPTEMBER 12, 2024

Clustering: An unsupervised Machine Learning technique that groups similar data points based on their inherent similarities. 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.

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Heartbeat

JANUARY 25, 2023

Then they use these patterns to understand the public’s behavior and predict the election results, thus making more informed political strategies based on population clusters. The model-building process involves Natural Language Processing, Deep Learning techniques, and Python libraries. .

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