Analytics, Supervised Learning and Support Vector Machines

One Class Classification Using Support Vector Machines

Analytics Vidhya

JUNE 3, 2022

Introduction Classification problems are often solved using supervised learning algorithms such as Random Forest Classifier, Support Vector Machine, Logistic Regressor (for binary class classification) etc. The post One Class Classification Using Support Vector Machines appeared first on Analytics Vidhya.

Support Vector Machines

Support Vector Machines Supervised Learning Data Science Algorithm

Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

MAY 27, 2024

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. In this blog, we will explore the details of both approaches and navigate through their differences. What is Generative AI?

K-nearest Neighbors

K-nearest Neighbors Supervised Learning AI AI

Exploring All Types of Machine Learning Algorithms

Pickl AI

JANUARY 21, 2025

Types of Machine Learning Algorithms Machine Learning has become an integral part of modern technology, enabling systems to learn from data and improve over time without explicit programming. The goal is to learn a mapping from inputs to outputs, allowing the model to make predictions on unseen data.

Machine Learning

Machine Learning Machine Learning Algorithm Decision Trees

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What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

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Pattern recognition

Dataconomy

FEBRUARY 26, 2025

This capability bridges various disciplines, leveraging techniques from statistics, machine learning, and artificial intelligence. Some key areas include: Big Data analytics: It helps in interpreting vast amounts of data to extract meaningful insights.

Support Vector Machines

Support Vector Machines Machine Learning Machine Learning Decision Trees

Supervised vs Unsupervised Learning: Key Differences

How to Learn Machine Learning

MARCH 25, 2025

It plays a crucial role in areas like customer segmentation, fraud detection, and predictive analytics. At the core of machine learning, two primary learning techniques drive these innovations. These are known as supervised learning and unsupervised learning.

Supervised Learning

Supervised Learning Machine Learning Machine Learning Algorithm

Classifiers in Machine Learning

Pickl AI

APRIL 13, 2025

Classification is a subset of supervised learning, where labelled data guides the algorithm to make predictions. Support Vector Machines (SVM) SVM finds the optimal hyperplane that separates classes with maximum margin. These models can detect subtle patterns that might be missed by human radiologists.

Machine Learning

Machine Learning Machine Learning Decision Trees K-nearest Neighbors

Understanding Associative Classification in Data Mining

Pickl AI

FEBRUARY 2, 2025

Classification: How it Differs from Association Rules Classification is a supervised learning technique that aims to predict a target or class label based on input features. Multi-itemset rules : These rules show associations among multiple items, often uncovering more complex patterns.

Data Mining

Data Mining Data Mining Data Mining Decision Trees

Five machine learning types to know

IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning types Machine learning algorithms fall into five broad categories: supervised learning, unsupervised learning, semi-supervised learning, self-supervised and reinforcement learning. the target or outcome variable is known). temperature, salary).

Machine Learning

Machine Learning Machine Learning Supervised Learning Clustering

10 Machine Learning Algorithms You Need to Know in 2024

Pickl AI

SEPTEMBER 16, 2024

This section will explore the top 10 Machine Learning algorithms that you should know in 2024. Linear Regression Linear regression is one of the simplest and most widely used algorithms in Machine Learning. Applications Predictive Analytics: Forecasting future trends based on historical data.

Machine Learning

Machine Learning Machine Learning Algorithm Decision Trees

Exploring the dynamic fusion of AI and the IoT

Dataconomy

MAY 25, 2023

On the other hand, artificial intelligence focuses on creating intelligent systems that can learn, reason, and make decisions. When AI and IoT converge, we witness a synergy where AI empowers IoT devices with advanced analytics, automation, and intelligent decision-making.

Internet of Things

Internet of Things Artificial Intelligence Artificial Intelligence AI

Everything you should know about AI models

Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. Let’s dig deeper and learn more about them!

K-nearest Neighbors

K-nearest Neighbors Decision Trees AI AI

Everything you should know about AI models

Dataconomy

APRIL 4, 2023

Reminder : Training data refers to the data used to train an AI model, and commonly there are three techniques for it: Supervised learning: The AI model learns from labeled data, which means that each data point has a known output or target value. Let’s dig deeper and learn more about them!

K-nearest Neighbors

K-nearest Neighbors Decision Trees AI AI

Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning

Machine Learning Machine Learning Data Science Big Data

Unleashing the Power of Applied Text Mining in Python: Revolutionize Your Data Analysis

Pickl AI

AUGUST 1, 2023

Text mining is also known as text analytics or Natural Language Processing (NLP). 7 Advantages of Text Mining Text mining, also known as text analytics, refers to the process of extracting useful information and insights from large volumes of unstructured text data. What are the common applications of text mining?

Data Analysis

Data Analysis Data Analysis Python Support Vector Machines

Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

JULY 12, 2024

Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data. There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Python Natural Language Processing

Understanding and Building Machine Learning Models

Pickl AI

NOVEMBER 18, 2024

The main types are supervised, unsupervised, and reinforcement learning, each with its techniques and applications. Supervised Learning In Supervised Learning , the algorithm learns from labelled data, where the input data is paired with the correct output. For unSupervised Learning tasks (e.g.,

Machine Learning

Machine Learning Machine Learning Algorithm Decision Trees

Big Data Syllabus: A Comprehensive Overview

Pickl AI

AUGUST 9, 2024

It also addresses security, privacy concerns, and real-world applications across various industries, preparing students for careers in data analytics and fostering a deep understanding of Big Data’s impact. Velocity It indicates the speed at which data is generated and processed, necessitating real-time analytics capabilities.

Big Data

Big Data Big Data Big Data Analytics Big Data Analytics

Must-Have Skills for a Machine Learning Engineer

Pickl AI

NOVEMBER 28, 2024

These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Supervised Learning Supervised learning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data. They are handy for high-dimensional data.

Machine Learning

Machine Learning Machine Learning ML ML

The Age of BioInformatics: Part 2

Heartbeat

OCTOBER 25, 2023

e) Big Data Analytics: The exponential growth of biological data presents challenges in storing, processing, and analyzing large-scale datasets. Supervised learning algorithms, such as support vector machines and random forests, have been extensively used in bioinformatics for tasks like classifying biological samples and predicting outcomes.

Machine Learning

Machine Learning Machine Learning Data Scientist Algorithm

Credit Card Fraud Detection Using Spectral Clustering

PyImageSearch

SEPTEMBER 16, 2024

Machine Learning Methods Machine learning methods ( Figure 7 ) can be divided into supervised, unsupervised, and semi-supervised learning techniques. Figure 7: Machine learning methods for identifying outliers or anomalies (source : Turing ). unusual network traffic patterns).

Clustering

Clustering Algorithm Machine Learning Machine Learning

Best Resources for Kids to learn Data Science with Python

Pickl AI

MAY 31, 2023

Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, Natural Language Processing , Statistics and Mathematics. After that, move towards unsupervised learning methods like clustering and dimensionality reduction.

Data Science

Data Science Python Data Scientist Machine Learning

Understanding the Synergy Between Artificial Intelligence & Data Science

Pickl AI

SEPTEMBER 23, 2024

Healthcare Data Science is revolutionising healthcare through predictive analytics, personalised medicine, and disease detection. Data Science continues to impact various industries, driving innovation and efficiency through data-driven insights and advanced analytics.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Data Science Machine Learning

Basic Data Science Terms Every Data Analyst Should Know

Pickl AI

SEPTEMBER 12, 2024

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.

Data Analyst

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One Class Classification Using Support Vector Machines

Generative vs Discriminative AI: Understanding the 5 Key Differences

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Exploring All Types of Machine Learning Algorithms

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Pattern recognition

Supervised vs Unsupervised Learning: Key Differences

Classifiers in Machine Learning

Understanding Associative Classification in Data Mining

Five machine learning types to know

10 Machine Learning Algorithms You Need to Know in 2024

Exploring the dynamic fusion of AI and the IoT

Everything you should know about AI models

Everything you should know about AI models

Data science vs. machine learning: What’s the difference?

Unleashing the Power of Applied Text Mining in Python: Revolutionize Your Data Analysis

Artificial Intelligence Using Python: A Comprehensive Guide

Understanding and Building Machine Learning Models

Big Data Syllabus: A Comprehensive Overview

Must-Have Skills for a Machine Learning Engineer

The Age of BioInformatics: Part 2

Credit Card Fraud Detection Using Spectral Clustering

Best Resources for Kids to learn Data Science with Python

Understanding the Synergy Between Artificial Intelligence & Data Science

Basic Data Science Terms Every Data Analyst Should Know

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