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Problem-solving tools offered by digital technology

Data Science Dojo

Ultimately, we can use two or three vital tools: 1) [either] a simple checklist, 2) [or,] the interdisciplinary field of project-management, and 3) algorithms and data structures. To the rescue (!): To recap, those twelve elements (e.g. What problem-solving tools next digital age has to offer Thanks to Moore’s law (e.g., IoT, Web 3.0,

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Journey to AI blog

Common machine learning algorithms for supervised learning include: K-nearest neighbor (KNN) algorithm : This algorithm is a density-based classifier or regression modeling tool used for anomaly detection. If a data point appears further away from a dense section of points, it is considered an anomaly.

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Everything to know about Anomaly Detection in Machine Learning

Pickl AI

Anomaly Detection in Machine Learning: An approach to data analysis and Machine Learning called “anomaly detection,” also referred to as “outlier detection,” focuses on finding data points or patterns that considerably differ from what is considered to be “normal” or anticipated behaviour.

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Predicting Heart Failure Survival with Machine Learning Models — Part II

Towards AI

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. In our exercise, we will try to deal with this imbalance by — Using a stratified k-fold cross-validation technique to make sure our model’s aggregate metrics are not too optimistic (meaning: too good to be true!)

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From Pixels to Places: Harnessing Geospatial Data with Machine Learning.

Towards AI

Created by the author with DALL E-3 Machine learning algorithms are the “cool kids” of the tech industry; everyone is talking about them as if they were the newest, greatest meme. Shall we unravel the true meaning of machine learning algorithms and their practicability?

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Five machine learning types to know

IBM Journey to AI blog

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

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8 of the Top Python Libraries You Should be Using in 2024

ODSC - Open Data Science

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. Without this library, data analysis wouldn’t be the same without pandas, which reign supreme with its powerful data structures and manipulation tools.

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