Mlearning.ai

MARCH 1, 2023

The K-Nearest Neighbors Algorithm Math Foundations: Hyperplanes, Voronoi Diagrams and Spacial Metrics. Throughout this article we’ll dissect the math behind one of the most famous, simple and old algorithms in all statistics and machine learning history: the KNN. Photo by Who’s Denilo ? Photo from here 2.1

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Towards AI

MARCH 21, 2023

By New Africa In this article, I will show how to implement a K-Nearest Neighbor classification with Tensorflow.js. KNN KNN (K-Nearest Neighbors) classification is a supervised machine learning algorithm used for classification tasks. TensorFlow.js TensorFlow.js

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AWS Machine Learning Blog

SEPTEMBER 8, 2023

Publishers can have repositories containing millions of images and in order to save money, they need to be able to reuse these images across articles. Finding the image that best matches an article in repositories of this scale can be a time-consuming, repetitive, manual task that can be automated.

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Towards AI

MAY 1, 2024

In this piece, we shall look at tips and tricks on how to perform particular GIS machine learning algorithms regardless of your expertise in GIS, if you are a fresh beginner with no experience or a seasoned expert in geospatial machine learning. R Studios and GIS In a previous article, I wrote about GIS and R., Decision Tree and R.

Machine Learning 95

Machine Learning Machine Learning K-nearest Neighbors Decision Trees 95

Towards AI

JULY 15, 2024

Hopefully, this article will serve as a roadmap for leveraging the power of R, a versatile programming language, for spatial analysis, data science and visualization within GIS contexts. R, GIS and Machine learning I have written about the amazing wonders of R for GIS in my previous articles, but I will sum it up.

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Dataconomy

MARCH 4, 2025

This article delves into the essential components of data mining, highlighting its processes, techniques, tools, and applications. By utilizing algorithms and statistical models, data mining transforms raw data into actionable insights. What is data mining?

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Towards AI

MAY 10, 2024

Photo by National Cancer Institute on Unsplash This article delves into medical image analysis, specifically focusing on the classification of brain tumors. The three weak learner models used for this implementation were k-nearest neighbors, decision trees, and naive Bayes.

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Dataversity

JUNE 4, 2021

In this article, we will discuss the KNN Classification method of analysis. What Is the KNN Classification Algorithm? The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories.

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K-nearest Neighbors Algorithm Data Analysis Data Analysis 52

ODSC - Open Data Science

DECEMBER 14, 2023

Using Guardrails for Trustworthy AI, Projected AI Trends for 2024, and the Top Remote AI Jobs in 2024 How to Use Guardrails to Design Safe and Trustworthy AI In this article, you’ll get a better understanding of guardrails within the context of this post and how to set them at each stage of AI design and development. Essential AI Raises $56.5

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Towards AI

APRIL 14, 2023

In the second part, I will present and explain the four main categories of XML algorithms along with some of their limitations. However, typical algorithms do not produce a binary result but instead, provide a relevancy score for which labels are the most appropriate. Thus tail labels have an inflated score in the metric.

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Towards AI

JANUARY 26, 2024

This article will explain the concept of hyperparameter tuning and the different methods that are used to perform this tuning, and their implementation using python Photo by Denisse Leon on Unsplash Table of Content Model Parameters Vs Model Hyperparameters What is hyperparameter tuning? What is hyperparameter tuning?

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Mlearning.ai

APRIL 6, 2023

However, with a wide range of algorithms available, it can be challenging to decide which one to use for a particular dataset. In this article, we will discuss some of the factors to consider while selecting a classification & Regression machine learning algorithm based on the characteristics of the data.

Machine Learning 52

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Pickl AI

JULY 31, 2023

In this article, we will delve into the differences and characteristics of these two methods, shedding light on their unique advantages and use cases. Examples of Eager Learning Algorithms: Logistic Regression : A classic Eager Learning algorithm used for binary classification tasks. Eager Learning Algorithms: How does it work?

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Pickl AI

JULY 17, 2023

Machine Learning is a subset of artificial intelligence (AI) that focuses on developing models and algorithms that train the machine to think and work like a human. The following blog will focus on Unsupervised Machine Learning Models focusing on the algorithms and types with examples. What is Unsupervised Machine Learning?

Machine Learning 52

Machine Learning Machine Learning Clustering K-nearest Neighbors 52

Dataconomy

APRIL 4, 2023

In this article, we will explain everything you need to know about AI models, such as the best ones, their types, and how to choose them. Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. What is an AI model?

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Dataconomy

APRIL 4, 2023

In this article, we will explain everything you need to know about AI models, such as the best ones, their types, and how to choose them. Artificial Intelligence (AI) models are the building blocks of modern machine learning algorithms that enable machines to learn and perform complex tasks. What is an AI model?

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Mlearning.ai

FEBRUARY 17, 2023

ML algorithms can be broadly divided into supervised learning , unsupervised learning , and reinforcement learning. In this article, I will cover all of them. Strictly, everything that I said earlier is based on Machine learning algorithms and, of course, strong math and theory of algorithms behind them.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

Pickl AI

AUGUST 22, 2023

Among the multitude of techniques available to enhance the efficacy of Machine Learning algorithms, feature scaling stands out as a fundamental process. Scaling ensures that all features contribute proportionally to the learning process, preventing any one feature from dominating the algorithm’s behavior.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

PyImageSearch

JUNE 19, 2023

Each service uses unique techniques and algorithms to analyze user data and provide recommendations that keep us returning for more. By analyzing how users have interacted with items in the past, we can use algorithms to approximate the utility function and make personalized recommendations that users will love.

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Mlearning.ai

JULY 4, 2023

This article will delve into the fascinating process of extracting and modeling such data, leveraging the power of pyCaret, a low-code machine learning library in Python. The results we obtained were astonishing, with the KNN algorithm demonstrating near-perfect accuracy in predicting the races of people based on their tweets.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 52

Heartbeat

SEPTEMBER 22, 2023

Introduction In the world of machine learning, where algorithms learn from data to make predictions, it’s important to get the best out of our models. In this article, we will explore how to tune hyperparameters, making complex ideas easy to understand, especially for those just starting out in machine learning. random_state=0) 3.3.

Machine Learning 64

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ODSC - Open Data Science

JANUARY 5, 2024

Scikit-learn A machine learning powerhouse, Scikit-learn provides a vast collection of algorithms and tools, making it a go-to library for many data scientists. In this article, I will provide my top five reasons for using the Seaborn library to create data visualizations with Python. What’s next for me and these top Python libraries?

Python 52

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Pickl AI

NOVEMBER 18, 2024

The article also addresses challenges like data quality and model complexity, highlighting the importance of ethical considerations in Machine Learning applications. Key steps involve problem definition, data preparation, and algorithm selection. The algorithm tries to find hidden patterns or groupings in the data.

Machine Learning 52

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Mlearning.ai

MAY 21, 2023

This is going to be a very interesting blog, so without any further due, let’s do it… Read the full article here —  [link] Machine Learning is a rapidly evolving field that has gained immense popularity due to its ability to make predictions and decisions based on patterns and data. Checkout the code walkthrough [link] 13.

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Machine Learning Machine Learning Python K-nearest Neighbors 52

Mlearning.ai

MAY 23, 2023

Read the full article here —  [link] For final-year students pursuing a degree in computer science or related disciplines, engaging in machine learning projects can be an excellent way to consolidate theoretical knowledge, gain practical experience, and showcase their skills to potential employers. Checkout the code walkthrough [link] 13.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Python 52

The MLOps Blog

DECEMBER 19, 2022

In this article, we will talk about feasible techniques to deal with such a large-scale ML Classification model. In this article, you will learn: 1 What are some examples of large-scale ML classification models? The selection of the correct loss function plays a pivotal role in the success of the algorithm.

ML 52

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Pickl AI

SEPTEMBER 12, 2024

Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. This article aims to equip you with a solid foundation of essential Data Science terms, empowering you to navigate the industry confidently.

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Mlearning.ai

FEBRUARY 15, 2023

Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. Figure 11 Model Architecture The algorithms and models used for the first three classifiers are essentially the same. K-Nearest Neighbou r: The k-Nearest Neighbor algorithm has a simple concept behind it.

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DagsHub

JUNE 24, 2024

This article explains image embeddings and the technology that powers them while presenting industry use cases and best practices to implement image embeddings in your organization. In which a machine learning algorithm is trained with a small dataset, in this case made of embeddings.

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Mlearning.ai

JUNE 6, 2023

Read the full article here —  [link] Though textbooks and other study materials will provide you with all the knowledge that you need to know about any technology but you can’t really master that technology until and unless you work on some real-time projects. However, manual detection of leaf diseases is time-consuming and often inaccurate.

Machine Learning 52

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Mlearning.ai

MAY 23, 2023

In this article, we will explore some common data science interview questions that will help you prepare and increase your chances of success. Let us first understand the meaning of bias and variance in detail: Bias: It is a kind of error in a machine learning model when an ML Algorithm is oversimplified. What is Data Science?

Data Science 52

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Pickl AI

JULY 26, 2023

K-Nearest Neighbors with Small k I n the k-nearest neighbours algorithm, choosing a small value of k can lead to high variance. A smaller k implies the model is influenced by a limited number of neighbours, causing predictions to be more sensitive to noise in the training data.

Machine Learning 52

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DagsHub

DECEMBER 23, 2024

For instance, given a certain sample if the active learning algorithm is uncertain about the correct response it can send the sample to the human annotator. Key Characteristics Synthetic Data Generation : Query synthesis algorithms actively generate new training examples rather than selecting from an existing pool.

Deep Learning 52

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Towards AI

JULY 19, 2023

Dealing with imbalanced data is pretty common in the real-world and these articles by German Lahera and on DataCamp are good places to learn about them. Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., among supervised models and k-nearest neighbors, DBSCAN, etc.,

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Dataconomy

APRIL 30, 2025

It helps bring different features to a common scale, which is particularly important for algorithms that rely on the distance between data points. In this article, we will explore the various aspects of normalization, including its types, use cases, and guidelines for implementation.

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