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Data mining hacks 101: Listing down best techniques for beginners

Data Science Dojo

Data mining has become increasingly crucial in today’s digital age, as the amount of data generated continues to skyrocket. In fact, it’s estimated that by 2025, the world will generate 463 exabytes of data every day, which is equivalent to 212,765,957 DVDs per day!

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Text Classification Using Machine Learning Algorithm in R

Heartbeat

Data mining, text classification, and information retrieval are just a few applications. To extract themes from a corpus of text data and then use these themes as features in text classification algorithms, topic modeling can be used in text classification. Naive Bayes is commonly used for spam classification.

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Elevating business decisions from gut feelings to data-driven excellence

Dataconomy

Decision intelligence is an innovative approach that blends the realms of data analysis, artificial intelligence, and human judgment to empower businesses with actionable insights. Think of decision intelligence as a synergy between the human mind and cutting-edge algorithms. AI algorithms play a crucial role in decision intelligence.

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

Pickl AI

Text Vectorization Techniques Text vectorization is a crucial step in text mining, where text data is transformed into numerical representations that can be processed by Machine Learning algorithms. Sentiment analysis techniques range from rule-based approaches to more advanced machine learning algorithms.

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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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Classification vs. Clustering

Pickl AI

Machine Learning is a subset of Artificial Intelligence and Computer Science that makes use of data and algorithms to imitate human learning and improving accuracy. Being an important component of Data Science, the use of statistical methods are crucial in training algorithms in order to make classification.

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

IBM Journey to AI blog

Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis. Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention.