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Data mining

Dataconomy

Data mining is a fascinating field that blends statistical techniques, machine learning, and database systems to reveal insights hidden within vast amounts of data. Businesses across various sectors are leveraging data mining to gain a competitive edge, improve decision-making, and optimize operations.

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

Pickl AI

Examples of Eager Learning Algorithms: Logistic Regression : A classic Eager Learning algorithm used for binary classification tasks. Support Vector Machines (SVM) : SVM is a powerful Eager Learning algorithm used for both classification and regression tasks. Eager Learning Algorithms: How does it work?

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A Guide to Unsupervised Machine Learning Models | Types | Applications

Pickl AI

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. It entails developing computer programs that can improve themselves on their own based on expertise or data. What is Unsupervised Machine Learning?

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Fundamentals of Recommendation Systems

PyImageSearch

Each service uses unique techniques and algorithms to analyze user data and provide recommendations that keep us returning for more. By analyzing how users have interacted with items in the past, we can use algorithms to approximate the utility function and make personalized recommendations that users will love.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

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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[Updated] 100+ Top Data Science Interview Questions

Mlearning.ai

Read the full blog here —  [link] Data Science Interview Questions for Freshers 1. What is Data Science? Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. Some algorithms that have low bias are Decision Trees, SVM, etc.

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From prediction to prevention: Machines’ struggle to save our hearts

Dataconomy

The time has come for us to treat ML and AI algorithms as more than simple trends. Several data mining and neural network techniques have been employed to gauge the severity of heart disease but the prediction of it is a different subject. The decision tree algorithm used to select features is called the C4.5