IBM Journey to AI blog

DECEMBER 20, 2023

Supervised machine learning Supervised machine learning is a type of machine learning where the model is trained on a labeled dataset (i.e., Classification algorithms —predict categorical output variables (e.g., “junk” or “not junk”) by labeling pieces of input data.

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Machine Learning Machine Learning Supervised Learning Clustering 92

IBM Journey to AI blog

DECEMBER 19, 2023

This type of machine learning is useful in known outlier detection but is not capable of discovering unknown anomalies or predicting future issues. Regression modeling is a statistical tool used to find the relationship between labeled data and variable data.

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ODSC - Open Data Science

JANUARY 5, 2024

Scikit-learn A machine learning powerhouse, Scikit-learn provides a vast collection of algorithms and tools, making it a go-to library for many data scientists. It is easy to use, with a well-documented API and a wide range of tutorials and examples available.

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Python K-nearest Neighbors Data Science Data Visualization 52

Pickl AI

DECEMBER 9, 2024

Summary: Inductive bias in Machine Learning refers to the assumptions guiding models in generalising from limited data. By managing inductive bias effectively, data scientists can improve predictions, ensuring models are robust and well-suited for real-world applications.

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Pickl AI

JULY 26, 2023

The concepts of bias and variance in Machine Learning are two crucial aspects in the realm of statistical modelling and machine learning. Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models.

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Machine Learning Machine Learning Cross Validation Decision Trees 52

Pickl AI

SEPTEMBER 12, 2024

Data Science is the art and science of extracting valuable information from data. It encompasses data collection, cleaning, analysis, and interpretation to uncover patterns, trends, and insights that can drive decision-making and innovation.

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Data Analyst Data Science Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 18, 2024

The algorithm you select depends on the nature of the problem and the type of data you have. spam detection), you might choose algorithms like Logistic Regression , Decision Trees, or Support Vector Machines. customer segmentation), clustering algorithms like K-means or hierarchical clustering might be appropriate.

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