Analytics Vidhya

JANUARY 20, 2022

Introduction Boosting is a key topic in machine learning. As a result, in this article, we are going to define and explain Machine Learning boosting. With the help of “boosting,” machine learning models are […]. Numerous analysts are perplexed by the meaning of this phrase.

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Flipboard

NOVEMBER 26, 2023

Today, I've been asked to explain algorithms in five … Hello world. My name is David J. Malan and I'm a professor of computer science at Harvard University.

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Analytics Vidhya

OCTOBER 31, 2022

Introduction Hello AI&ML Engineers, as you all know, Artificial Intelligence (AI) and Machine Learning Engineering are the fastest growing filed, and almost all industries are adopting them to enhance and expedite their business decisions and needs; for the same, they are working on various aspects […].

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Towards AI

NOVEMBER 5, 2023

A deep dive into explainability for business leadersImage by Don Kaluarachchi (author) In this article, we are going to look into the world of machine learning explainability. We will demystify the secrets behind those decision-making algorithms that often feel like they are locked away in a black box.

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IBM Journey to AI blog

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029.

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Towards AI

FEBRUARY 16, 2024

Image adapted from Adobe Stock Machine learning is becoming a significant tool in the field of chemistry, providing new opportunities in various areas such as drug discovery and materials science. Recently, the combination of machine learning and chemistry has made significant progress.

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Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. The truth is, I couldn’t find anything.

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

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Flipboard

OCTOBER 13, 2023

Machine learning (ML) may be able to … Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important.

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Analytics Vidhya

JUNE 1, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In this article, I’m gonna explain about DBSCAN algorithm. The post Understand The DBSCAN Clustering Algorithm! appeared first on Analytics Vidhya.

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Algorithm Explainability Data Science ML 267

Pickl AI

JUNE 2, 2023

One of the most popular algorithms in Machine Learning are the Decision Trees that are useful in regression and classification tasks. Decision trees are easy to understand, and implement therefore, making them ideal for beginners who want to explore the field of Machine Learning. How Decision Tree Algorithm works?

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Analytics Vidhya

APRIL 30, 2021

Before I get into explaining the random forest algorithms and. The post Getting into Random Forest Algorithms appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon.

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Analytics Vidhya

MAY 11, 2020

A Simple Analogy to Explain Decision Tree vs. Random Forest Let’s start with a thought experiment that will illustrate the difference between a decision. The post Decision Tree vs. Random Forest – Which Algorithm Should you Use? appeared first on Analytics Vidhya.

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Analytics Vidhya

MARCH 14, 2023

According to a survey or study, AI […] The post What are Explainability AI Techniques? The quality of AI is what matters most and is one of the vital causes of the failure of any business or organization. Why do We Need it? appeared first on Analytics Vidhya.

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Marktechpost

JANUARY 1, 2024

In machine learning (ML) research at Meta, the challenges of debugging at scale have led to the development of HawkEye, a powerful toolkit addressing the complexities of monitoring, observability, and debuggability.

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Mlearning.ai

OCTOBER 2, 2023

Learn how to use them to avoid the biggest scare in ML: overfitting and underfitting. Photo by Arseny Togulev on Unsplash If you’re working with a dataset and trying to build a machine learning model, you probably don’t need all the data and columns that feed into your model. Here’s the overview.

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Machine Learning Explainability Auto-complete Algorithm 98

Flipboard

NOVEMBER 21, 2023

MATLAB   is a popular programming tool for a wide range of applications, such as data processing, parallel computing, automation, simulation, machine learning, and artificial intelligence. Because we have a model of the system and faults are rare in operation, we can take advantage of simulated data to train our algorithm.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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Towards AI

FEBRUARY 3, 2024

Photo by Markus Winkler on Unsplash Let’s get started: Machine Learning has become the most demanding and powerful tool in different domains of several industries in this digital era to solve many complex problems by revolutionizing the way of approaching those problems.

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Towards AI

MARCH 17, 2024

Alongside this, there is a second boom in XAI or Explainable AI. Explainable AI is focused on helping us poor, computationally inefficient humans understand how AI “thinks.” We will then explore some techniques for building glass-box or explainable models. Ultimately these definitions end up being almost circular!

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Explainable AI Explainability Neural Network AI 97

Unite.AI

MARCH 14, 2024

For many institutional investors, the answer is likely to be no – that the potential benefits of AI just aren’t worth the risk associated with a process they aren’t able to understand, much less explain to their boards and clients. But there is a way out of this dilemma.

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Analytics Vidhya

AUGUST 22, 2021

The post Granger Causality in Time Series – Explained using Chicken and Egg problem appeared first on Analytics Vidhya. ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction The purpose of this article is to understand what is granger.

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Analytics Vidhya

JUNE 12, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Agglomerative Clustering using Single Linkage (Source) As we all know, The post Single-Link Hierarchical Clustering Clearly Explained! appeared first on Analytics Vidhya.

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Heartbeat

OCTOBER 11, 2023

A key component of artificial intelligence is training algorithms to make predictions or judgments based on data. This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. What is Machine Learning?

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Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model.

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Unite.AI

MAY 8, 2024

What initially got you interested in machine learning? My interest in machine learning (ML) was a gradual process. During my school years, I spent a lot of time studying math, probability theory, and statistics, and got an opportunity to play with classical machine learning algorithms such as linear regression and KNN.

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Becoming Human

MAY 11, 2023

Machine learning operations, or MLOps, are the set of practices and tools that aim to streamline and automate the machine learning lifecycle. MLOps projects are projects that focus on implementing machine learning operations best practices into a company’s existing software development and deployment process.

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Heartbeat

SEPTEMBER 25, 2023

Photo by National Cancer Institute on Unsplash Machine learning has recently been a game-changer in healthcare and medical diagnosis. This is where machine learning comes in. Furthermore, machine learning algorithms are revolutionizing the concept of personalized medicine.

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Viso.ai

MAY 10, 2024

Whether you’re building a consumer app to recognize plant species or an enterprise tool to monitor office security camera footage, you are going to need to build a Machine Learning (ML) model to provide the core functionality. Building a machine learning model consists of 7 high-level steps: 1.

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ODSC - Open Data Science

FEBRUARY 10, 2023

8 Potential Technosignatures Found Beyond Our Solar System Thanks to Machine Learning With the aid of machine learning, 8 potential technosignatures have been found around five nearby stars. The team found the new algorithm excelled by organizing the data from telescopes into categories.

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Heartbeat

SEPTEMBER 7, 2023

In the ever-evolving landscape of machine learning and artificial intelligence, understanding and explaining the decisions made by models have become paramount. Enter Comet , that streamlines the model development process and strongly emphasizes model interpretability and explainability. Why Does It Matter?

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PyImageSearch

MAY 8, 2023

Home Table of Contents Introduction to Causality in Machine Learning Correlation and Causation Case Study 1: A “Marvelous” Problem Scenario 1: A Direct Cause Scenario 2: Reversing the Cause and Effect Scenario 3: Investigating a Hidden Cause Causal Thinking Case Study 2: Food App Conundrum Quiz Time! Let’s find out.

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Unite.AI

JANUARY 15, 2024

Moving away from traditional trial-and-error methods, this innovative approach combines automated experiments with machine learning. By rapidly conducting a multitude of chemical reactions, they provide a rich dataset for the AI algorithms to analyze.

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Marktechpost

APRIL 28, 2024

and also allows the students to build an understanding of machine learning algorithms, including supervised, unsupervised, reinforcement, etc. The course covers the common terminologies of AI, including neural networks, machine learning, deep learning, etc., and what AI can and cannot do.

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ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). In this comprehensive guide, I will demystify machine learning, breaking it down into digestible concepts for beginners. What is Machine Learning? predicting house prices).

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Marktechpost

JULY 6, 2023

As a result of recent technological advances in machine learning (ML), ML models are now being used in a variety of fields to improve performance and eliminate the need for human labor. As a result, it becomes necessary for humans to comprehend these algorithms and their workings on a deeper level.

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