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Machine learning algorithms

Dataconomy

Machine learning algorithms represent a transformative leap in technology, fundamentally changing how data is analyzed and utilized across various industries. What are machine learning algorithms? Regression: Focuses on predicting continuous values, such as forecasting sales or estimating property prices.

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Feature scaling: A way to elevate data potential

Data Science Dojo

Feature Engineering is a process of using domain knowledge to extract and transform features from raw data. These features can be used to improve the performance of Machine Learning Algorithms. Normalization A feature scaling technique is often applied as part of data preparation for machine learning.

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Supervised vs Unsupervised Learning: Key Differences

How to Learn Machine Learning

It uses unlabeled data where only inputs are given without any predefined outputs. The ML algorithm tries to find hidden patterns and structures in this data. It groups similar data points or identifies outliers without prior guidance. Unsupervised learning deals with data that has not been labeled.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Jupyter notebooks are widely used in AI for prototyping, data visualisation, and collaborative work. Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning.

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Credit Card Fraud Detection Using Spectral Clustering

PyImageSearch

Home Table of Contents Credit Card Fraud Detection Using Spectral Clustering Understanding Anomaly Detection: Concepts, Types and Algorithms What Is Anomaly Detection? Jump Right To The Downloads Section Understanding Anomaly Detection: Concepts, Types, and Algorithms What Is Anomaly Detection? Looking for the source code to this post?

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Understanding Machine Learning algorithms and effective data handling are also critical for success in the field.

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Understanding and Building Machine Learning Models

Pickl AI

Key Takeaways Machine Learning Models are vital for modern technology applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance. Ethical considerations are crucial in developing fair Machine Learning solutions.