Data Mining and Supervised Learning

Understanding Associative Classification in Data Mining

Pickl AI

FEBRUARY 2, 2025

Summary: Associative classification in data mining combines association rule mining with classification for improved predictive accuracy. Despite computational challenges, its interpretability and efficiency make it a valuable technique in data-driven industries. Lets explore each in detail.

Data Mining

Data Mining Data Mining Data Mining Decision Trees

Fundamentals of Data Mining

Data Science 101

OCTOBER 31, 2019

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Machine learning provides the technical basis for data mining.

Data Mining

Data Mining Data Mining Data Mining Data Science

How to tackle lack of data: an overview on transfer learning

Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

Supervised Learning

Supervised Learning Machine Learning Machine Learning Deep Learning

Webinars

How to Achieve High-Accuracy Results When Using LLMs

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Data Science Journey Walkthrough – From Beginner to Expert

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. Semi-Supervised Learning.

Data Science

Data Science Exploratory Data Analysis Machine Learning Machine Learning

How To Learn Python For Data Science?

Pickl AI

NOVEMBER 4, 2024

Use cases include visualising distributions, relationships, and categorical data, effortlessly enhancing the aesthetics of your plots. Scikit-learn Scikit-learn is the go-to library for Machine Learning in Python. It offers simple and efficient tools for data mining and Data Analysis.

Data Science

Data Science Python Machine Learning Machine Learning

A Guide to Unsupervised Machine Learning Models | Types | Applications

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. Less accurate and trustworthy method.

Machine Learning

Machine Learning Machine Learning K-nearest Neighbors Clustering

Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

JULY 6, 2023

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.

Machine Learning

Machine Learning Machine Learning Data Science Big Data

Basic Data Science Terms Every Data Analyst Should Know

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.

Data Analyst

Data Analyst Data Science Machine Learning Machine Learning

Artificial Intelligence Using Python: A Comprehensive Guide

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.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Python Natural Language Processing

Standard LLMs are not enough. How to make them work for your business

Snorkel AI

OCTOBER 6, 2023

Pre-training with unstructured data Pre-training with unstructured data sounds simple: gather proprietary data from across your organization and dump it all into a self-supervised learning pipeline. For a proprietary general-purpose model, such public data sets may be sufficient.

Data Science

Data Science Supervised Learning Data Mining Data Mining

Understanding the Synergy Between Artificial Intelligence & Data Science

Pickl AI

SEPTEMBER 23, 2024

Synergy Between Artificial Intelligence and Data Science AI and Data Science complement each other through their unique but interconnected roles in data processing and analysis. Data Science involves extracting insights from structured and unstructured data using statistical methods, data mining, and visualisation techniques.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Data Science Machine Learning

Standard LLMs are not enough. How to make them work for your business

Snorkel AI

OCTOBER 6, 2023

Pre-training with unstructured data Pre-training with unstructured data sounds simple: gather proprietary data from across your organization and dump it all into a self-supervised learning pipeline. For a proprietary general-purpose model, such public data sets may be sufficient.

Data Science

Data Science Supervised Learning Data Mining Data Mining

Unleashing the Power of Applied Text Mining in Python: Revolutionize Your Data Analysis

Pickl AI

AUGUST 1, 2023

Future directions in text mining include improving language understanding with the help of deep learning models, developing better techniques for multilingual text analysis, and integrating text mining with other domains like image and video analysis. Frequently Asked Questions How does text mining differ from data mining?

Data Analysis

Data Analysis Data Analysis Python Support Vector Machines

MLOps and the evolution of data science

IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science

Data Science Machine Learning Machine Learning ML

Standard LLMs are not enough. How to make them work for your business

Snorkel AI

OCTOBER 6, 2023

Pre-training with unstructured data Pre-training with unstructured data sounds simple: gather proprietary data from across your organization and dump it all into a self-supervised learning pipeline. For a proprietary general-purpose model, such public data sets may be sufficient.

Data Scientist

Data Scientist Data Science Supervised Learning Data Mining

Eager Learning and Lazy Learning in Machine Learning: A Comprehensive Comparison

Pickl AI

JULY 31, 2023

Understanding Eager Learning Eager Learning, also known as “Eager Supervised Learning,” is a widely used approach in Machine Learning. In this paradigm, the model is trained on a labeled dataset before making predictions on new, unseen data.

Machine Learning

Machine Learning Machine Learning K-nearest Neighbors Algorithm

Data Science Current

Understanding Associative Classification in Data Mining

Fundamentals of Data Mining

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How to tackle lack of data: an overview on transfer learning

Webinars

Data Science Journey Walkthrough – From Beginner to Expert

How To Learn Python For Data Science?

A Guide to Unsupervised Machine Learning Models | Types | Applications

Data science vs. machine learning: What’s the difference?

Basic Data Science Terms Every Data Analyst Should Know

Artificial Intelligence Using Python: A Comprehensive Guide

Standard LLMs are not enough. How to make them work for your business

Understanding the Synergy Between Artificial Intelligence & Data Science

Standard LLMs are not enough. How to make them work for your business

Unleashing the Power of Applied Text Mining in Python: Revolutionize Your Data Analysis

MLOps and the evolution of data science

Standard LLMs are not enough. How to make them work for your business

Eager Learning and Lazy Learning in Machine Learning: A Comprehensive Comparison

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