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Top 8 Machine Learning Algorithms

Data Science Dojo

Support Vector Machines (SVM): This algorithm finds a hyperplane that best separates data points of different classes in high-dimensional space. Decision Trees: These work by asking a series of yes/no questions based on data features to classify data points. Balancing these trade-offs is essential.

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From RAG to fabric: Lessons learned from building real-world RAGs at GenAIIC – Part 2

AWS Machine Learning Blog

Oil and gas data analysis – Before beginning operations at a well a well, an oil and gas company will collect and process a diverse range of data to identify potential reservoirs, assess risks, and optimize drilling strategies. Consider a financial data analysis system. What caused inflation in 2021?

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

ODSC - Open Data Science

Without this library, data analysis wouldn’t be the same without pandas, which reign supreme with its powerful data structures and manipulation tools. Pandas provides a fast and efficient way to work with tabular data. It is widely used in data science, finance, and other fields where data analysis is essential.

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

IBM Journey to AI blog

As organizations collect larger data sets with potential insights into business activity, detecting anomalous data, or outliers in these data sets, is essential in discovering inefficiencies, rare events, the root cause of issues, or opportunities for operational improvements.

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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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AWS empowers sales teams using generative AI solution built on Amazon Bedrock

AWS Machine Learning Blog

At events, our teams now approach customer interactions armed with comprehensive, up-to-date information on demand. To maintain the integrity of our core data, we do not retain or use the prompts or the resulting account summary for model training.

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Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Data Cleaning: Raw data often contains errors, inconsistencies, and missing values. Data cleaning identifies and addresses these issues to ensure data quality and integrity. Data Visualisation: Effective communication of insights is crucial in Data Science.