Smart Data Collective

JUNE 4, 2021

Here are the chronological steps for the data science journey. First of all, it is important to understand what data science is and is not. Data science should not be used synonymously with data mining. Mathematics, statistics, and programming are pillars of data science. Clustering (Unsupervised).

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Pickl AI

AUGUST 1, 2023

Introduction to Applied Text Mining in Python Before going ahead, it is important to understand, What is Text Mining in Python? Text mining is also known as text analytics or Natural Language Processing (NLP). It helps in discovering hidden patterns and organizing text data into meaningful clusters.

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Pickl AI

SEPTEMBER 23, 2024

AI is making a difference in key areas, including automation, language processing, and robotics. Natural Language Processing: NLP helps machines understand and generate human language, enabling technologies like chatbots and translation.

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Towards AI

MAY 3, 2023

Solving Machine Learning Tasks with MLCoPilot: Harnessing Human Expertise for Success Many of us have made use of large language models (LLMs) like ChatGPT to generate not only text and images but also code, including machine learning code. Vector databases can store them and are designed for search and data mining.

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Pickl AI

MAY 10, 2023

Certainly, these predictions and classification help in uncovering valuable insights in data mining projects. ML algorithms fall into various categories which can be generally characterised as Regression, Clustering, and Classification. Both the hierarchical clustering and contentious clustering methods are seen as dendrogram.

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Dataconomy

JUNE 13, 2023

At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.

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Pickl AI

MARCH 7, 2024

Applications: It is extensively used for statistical analysis, data visualisation, and machine learning tasks such as regression, classification, and clustering. Recent Advancements: The R community continues to release updates and packages, expanding its capabilities in data visualisation and machine learning algorithms in 2024.

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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. What are the best data preprocessing tools of 2023?

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Pickl AI

JULY 12, 2024

Pandas: A powerful library for data manipulation and analysis, offering data structures and operations for manipulating numerical tables and time series data. Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models.

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Pickl AI

JULY 18, 2023

Its simplicity, versatility, and extensive range of libraries make it a favorite choice among Data Scientists. However, with libraries like NumPy, Pandas, and Matplotlib, Python offers robust tools for data manipulation, analysis, and visualization. Advanced Analytics: SAS offers a comprehensive set of advanced analytics capabilities.

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Pickl AI

JULY 17, 2023

Unsupervised Learning Algorithms Unsupervised Learning Algorithms tend to perform more complex processing tasks in comparison to supervised learning. However, unsupervised learning can be highly unpredictable compared to natural learning methods. K-Means Clustering: K-means is a popular and widely used clustering algorithm.

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Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.

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Data Science Blog

FEBRUARY 23, 2023

My point is, the more data you have, and the bigger computation resource you have, the better performance you get. In other words, machine learning has scalability with data and parameters. This characteristic is clearly observed in models in natural language processing (NLP) and computer vision (CV) like in the graphs below.

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Data Science Dojo

AUGUST 30, 2023

It leverages algorithms to parse data, learn from it, and make predictions or decisions without being explicitly programmed. From decision trees and neural networks to regression models and clustering algorithms, a variety of techniques come under the umbrella of machine learning.

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