Decision Trees, Predictive Analytics and Supervised Learning

Decision Trees

Predictive Analytics

Supervised Learning

Exploring All Types of Machine Learning Algorithms

Pickl AI

JANUARY 21, 2025

Summary: Machine Learning algorithms enable systems to learn from data and improve over time. Key examples include Linear Regression for predicting prices, Logistic Regression for classification tasks, and Decision Trees for decision-making.

Machine Learning

Machine Learning Machine Learning Algorithm Decision Trees

Classifiers in Machine Learning

Pickl AI

APRIL 13, 2025

Summary: Classifier in Machine Learning involves categorizing data into predefined classes using algorithms like Logistic Regression and Decision Trees. Introduction Machine Learning has revolutionized how we process and analyse data, enabling systems to learn patterns and make predictions.

Machine Learning

Machine Learning Machine Learning Decision Trees K-nearest Neighbors

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Trending Sources

Understanding Associative Classification in Data Mining

Pickl AI

FEBRUARY 2, 2025

It identifies hidden patterns in data, making it useful for decision-making across industries. Compared to decision trees and SVM, it provides interpretable rules but can be computationally intensive. Key applications include fraud detection, customer segmentation, and medical diagnosis.

Data Mining

Data Mining Data Mining Data Mining Decision Trees

Webinars

Agent Tooling: Connecting AI to Your Tools, Systems & Data

Automation, Evolved: Your New Playbook for Smarter Knowledge Work

How to Modernize Manufacturing Without Losing Control

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

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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

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

K-nearest Neighbors

K-nearest Neighbors Decision Trees AI AI

10 Machine Learning Algorithms You Need to Know in 2024

Pickl AI

SEPTEMBER 16, 2024

Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Introduction Machine Learning (ML) has rapidly evolved over the past few years, becoming an integral part of various industries, from healthcare to finance.

Machine Learning

Machine Learning Machine Learning Algorithm Decision Trees

Exploring the dynamic fusion of AI and the IoT

Dataconomy

MAY 25, 2023

This enables them to extract valuable insights, identify patterns, and make informed decisions in real-time. AI algorithms can uncover hidden correlations within IoT data, enabling predictive analytics and proactive actions.

Internet of Things

Internet of Things Artificial Intelligence Artificial Intelligence AI

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. predicting house prices).

Machine Learning

Machine Learning Machine Learning Algorithm Decision Trees

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

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

IBM Journey to AI blog

JULY 6, 2023

One ride-hailing transportation company uses big data analytics to predict supply and demand, so they can have drivers at the most popular locations in real time. The company also uses data science in forecasting, global intelligence, mapping, pricing and other business decisions.

Machine Learning

Machine Learning Machine Learning Data Science Big Data

Comparison: Artificial Intelligence vs Machine Learning

Pickl AI

OCTOBER 24, 2024

Different ML types address various challenges, allowing machines to learn and adapt in diverse ways. Supervised Learning : This is the most common form of ML, where algorithms learn from labelled data. The system knows both the input and the desired output, enabling it to make predictions about new, unseen data.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Machine Learning 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. For example, it helps predict patient outcomes, optimise hospital operations, and discover new drugs. Finance: AI-driven algorithms analyse historical data to detect fraud and predict market trends.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Data Science Machine Learning

Backpropagation

Dataconomy

MARCH 20, 2025

It acts as a learning mechanism, continuously refining model predictions through a process that adjusts weights based on errors. This iterative enhancement is vital for applications in predictive analytics, from face and speech recognition systems to complex natural language processing tasks. What is backpropagation?

Predictive Analytics

Predictive Analytics Machine Learning Machine Learning Deep Learning

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

Data Science Current

Exploring All Types of Machine Learning Algorithms

Classifiers in Machine Learning

Webinars

Trending Sources

Understanding Associative Classification in Data Mining

Webinars

Five machine learning types to know

Everything you should know about AI models

Everything you should know about AI models

10 Machine Learning Algorithms You Need to Know in 2024

Exploring the dynamic fusion of AI and the IoT

Understanding and Building Machine Learning Models

Artificial Intelligence Using Python: A Comprehensive Guide

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

Comparison: Artificial Intelligence vs Machine Learning

Understanding the Synergy Between Artificial Intelligence & Data Science

Backpropagation

Supervised vs Unsupervised Learning: Key Differences

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