Remove Decision Trees Remove K-nearest Neighbors Remove Supervised Learning Remove Support Vector Machines
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Problem-solving tools offered by digital technology

Data Science Dojo

Zheng’s “Guide to Data Structures and Algorithms” Parts 1 and Part 2 1) Big O Notation 2) Search 3) Sort 3)–i)–Quicksort 3)–ii–Mergesort 4) Stack 5) Queue 6) Array 7) Hash Table 8) Graph 9) Tree (e.g.,

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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

A visual representation of discriminative AI – Source: Analytics Vidhya Discriminative modeling, often linked with supervised learning, works on categorizing existing data. Generative AI often operates in unsupervised or semi-supervised learning settings, generating new data points based on patterns learned from existing data.

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3 Greatest Algorithms for Machine Learning and Spatial Analysis.

Towards AI

For geographical analysis, Random Forest, Support Vector Machines (SVM), and k-nearest Neighbors (k-NN) are three excellent methods. So, Who Do I Have? Data Complexity: Offers insights on feature importance and effectively manages high-dimensional data.

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Eager Learning and Lazy Learning in Machine Learning: A Comprehensive Comparison

Pickl AI

Understanding Eager Learning Eager Learning, also known as “Eager Supervised Learning,” is a widely used approach in Machine Learning. It learns the relationship between features and class labels during training and then predicts the probability of an instance belonging to a specific class.

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Machine learning world easy-to-understand overview for beginners

Mlearning.ai

Basically, Machine learning is a part of the Artificial intelligence field, which is mainly defined as a technic that gives the possibility to predict the future based on a massive amount of past known or unknown data. ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning.

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

IBM Journey to AI blog

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

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Classification Algorithm in Machine Learning: A Comprehensive Guide

Pickl AI

In this blog, we will delve into the world of classification algorithms, exploring their basics, key algorithms, how they work, advanced topics, practical implementation, and the future of classification in Machine Learning. Instead, they memorise the training data and make predictions by finding the nearest neighbour.