Remove Events Remove Supervised Learning Remove Support Vector Machines
article thumbnail

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. But what is an anomaly and why is detecting it important?

article thumbnail

Are AI technologies ready for the real world?

Dataconomy

AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. Common choices include neural networks (used in deep learning), decision trees, support vector machines, and more. With the model selected, the initialization of parameters takes place.

AI 136
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Exploring the dynamic fusion of AI and the IoT

Dataconomy

This enables them to respond quickly to changing conditions or events. Here are some important machine learning techniques used in IoT: Supervised learning Supervised learning involves training machine learning models with labeled datasets.

article thumbnail

Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks. Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data.

article thumbnail

AI Drug Discovery: How It’s Changing the Game

Becoming Human

These branches include supervised and unsupervised learning, as well as reinforcement learning, and within each, there are various algorithmic techniques that are used to achieve specific goals, such as linear regression, neural networks, and support vector machines.

AI 139
article thumbnail

Big Data Syllabus: A Comprehensive Overview

Pickl AI

Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis. Students should understand the concepts of event-driven architecture and stream processing. Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets.

article thumbnail

Credit Card Fraud Detection Using Spectral Clustering

PyImageSearch

Anomaly detection ( Figure 2 ) is a critical technique in data analysis used to identify data points, events, or observations that deviate significantly from the norm. Machine Learning Methods Machine learning methods ( Figure 7 ) can be divided into supervised, unsupervised, and semi-supervised learning techniques.