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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Analytics Vidhya

DECEMBER 2, 2021

Before starting out directly with classification let’s talk about ML tasks in general. Machine Learning tasks are mainly divided into three types Supervised Learning — […]. The post Evaluating A Classification Model for Data Science appeared first on Analytics Vidhya.

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Dataconomy

MARCH 27, 2023

Robotic process automation vs machine learning is a common debate in the world of automation and artificial intelligence. However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. What is machine learning (ML)?

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Dataconomy

APRIL 27, 2022

Contrary to popular belief, the history of machine learning, which enables machines to learn tasks for which they are not specifically programmed, and train themselves in unfamiliar environments, goes back to 17th century. Machine learning is a powerful tool for implementing artificial intelligence technologies.

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Mlearning.ai

JUNE 20, 2023

“Self-Supervised methods […] are going to be the main method to train neural nets before we train them for difficult tasks” —  Yann LeCun Well! Let’s have a look at this Self-Supervised Learning! Let’s have a look at Self-Supervised Learning. That is why it is called Self -Supervised Learning.

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Mlearning.ai

OCTOBER 2, 2023

We’ll talk about supervised and unsupervised feature selection techniques. Learn how to use them to avoid the biggest scare in ML: overfitting and underfitting. The two main categories of feature selection are supervised and unsupervised machine learning techniques. Here’s the overview.

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PyImageSearch

MARCH 30, 2023

If you want a gentle introduction to machine learning for computer vision, you’re in the right spot. Here at PyImageSearch we’ve been helping people just like you master deep learning for computer vision. Also, you might want to check out our computer vision for deep learning program before you go.

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Analytics Vidhya

SEPTEMBER 27, 2023

Introduction In recent years, the integration of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Machine Learning (ML), has fundamentally transformed the landscape of text-based communication in businesses.

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Towards AI

JULY 22, 2023

This is similar to how machine learning (ML) can seem at first. In today’s post, we’re going to decode ten of the most common machine learning terms. This is precisely the process we emulate in cloud-based Machine Learning. Instead, it learns by finding patterns and structures in the input data.

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Pickl AI

AUGUST 6, 2023

Machine Learning has become a fundamental part of people’s lives and it typically holds two segments. It includes supervised and unsupervised learning. Supervised Learning deals with labels data and unsupervised learning deals with unlabelled data. What is Regression in ML?

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Mlearning.ai

FEBRUARY 17, 2023

A complete explanation of the most widely practical and efficient field, that nowadays has an impact on every industry Photo by Thomas T on Unsplash Machine learning has become one of the most rapidly evolving and popular fields of technology in recent years. How is it actually looks in a real life process of ML investigation?

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Mlearning.ai

APRIL 18, 2023

Understanding the DINOv2 Model, its Advantages, and its Applications in Computer Vision Introduction : Meta AI, has recently open-sourced DINOv2, a self-supervised learning method for training computer vision models. In this article, we will discuss what DINOv2 is, its advantages, applications, and conclusions. What is DINOv2?

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Dataconomy

SEPTEMBER 13, 2023

These labels provide crucial context for machine learning models, enabling them to make informed decisions and predictions. According to Gartner, a renowned research firm, by 2022, an astounding 70% of customer interactions are expected to flow through technologies like machine learning applications, chatbots, and mobile messaging.

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Dataconomy

MARCH 27, 2023

Robotic process automation vs machine learning is a common debate in the world of automation and artificial intelligence. However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. What is machine learning (ML)?

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Dataversity

JANUARY 22, 2024

The concept of application mapping is not new, but its […] The post Machine Learning Techniques for Application Mapping appeared first on DATAVERSITY. It provides a detailed view of how different applications interact, depend on each other, and contribute to the business processes.

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Mlearning.ai

JANUARY 31, 2023

Unlock the full potential of supervised learning with advanced techniques such as Regularization, Explainability, and more Continue reading on MLearning.ai »

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Heartbeat

OCTOBER 11, 2023

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? Machine learning algorithms can make predictions or classifications based on input data.

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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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phData

AUGUST 24, 2023

However, these models are evolving, with machine learning now playing an essential role in refining and improving the accuracy and efficiency of credit scoring and decisioning. How Does Machine Learning Impact These Models? Below are just some of the advantages provided by incorporating machine learning in credit models.

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ODSC - Open Data Science

MARCH 22, 2023

Be sure to check out his session, “ Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI ,” there! Anybody who has worked on a real-world ML project knows how messy data can be. Everybody knows you need to clean your data to get good ML performance. A common gripe I hear is: “Garbage in, garbage out.

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Google Research AI blog

FEBRUARY 8, 2023

This post covers two of our recent papers on AD, published in Transactions on Machine Learning Research (TMLR), that address the above challenges in unsupervised and semi-supervised settings. SPADE: Semi-supervised Pseudo-labeler Anomaly Detection with Ensembling Most semi-supervised learning methods (e.g.,

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Dataversity

DECEMBER 16, 2022

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements.

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DagsHub

AUGUST 2, 2023

ML teams have a very important core purpose in their organizations - delivering high-quality, reliable models, fast. With users’ productivity in mind, at DagHub we aimed for a solution that will provide ML teams with the whole process out of the box and with no extra effort.

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KDnuggets

DECEMBER 16, 2019

It is an annual tradition for Xavier Amatriain to write a year-end retrospective of advances in AI/ML, and this year is no different. Gain an understanding of the important developments of the past year, as well as insights into what expect in 2020.

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Heartbeat

FEBRUARY 15, 2023

It is a supervised learning methodology that predicts if a piece of text belongs to one category or the other. As a machine learning engineer, you start with a labeled data set that has vast amounts of text that have already been categorized. We pay our contributors, and we don’t sell ads.

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Dataconomy

APRIL 18, 2023

Understanding the basics of artificial intelligence Artificial intelligence is an interdisciplinary field of study that involves creating intelligent machines that can perform tasks that typically require human-like cognitive abilities such as learning, reasoning, and problem-solving.

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Dataconomy

JUNE 5, 2023

Regression in machine learning involves understanding the relationship between independent variables or features and a dependent variable or outcome. Machine learning has revolutionized the way we extract insights and make predictions from data. What is regression in machine learning?

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

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Pickl AI

AUGUST 7, 2023

Machine Learning is becoming increasingly popular in the current tech-savvy era. the application of Machine Learning is everywhere from image recognition to complex forecast models. However, the most complex and expensive type of Machine Learning trend in use is Labelled Data. How does Data Labelling Work?

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AWS Machine Learning Blog

DECEMBER 7, 2023

Amazon Forecast is a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts. Initially, daily forecasts for each country are formulated through ML models. His focus was building machine learning algorithms to simulate nervous network anomalies.

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. ML models are designed to learn from data and make predictions or decisions based on that data. The goal is to learn a mapping function from input variables to output variables based on examples of input-output pairs.

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. ML models are designed to learn from data and make predictions or decisions based on that data. The goal is to learn a mapping function from input variables to output variables based on examples of input-output pairs.

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Mlearning.ai

NOVEMBER 23, 2023

This Only Applies to Supervised Learning Introduction If you’re like me then you probably like a more intuitive way of doing things. When it comes to machine learning, we often have that one (or two or three) “go-to” model(s) that we tend to rely on for most problems. With Lazypredict.

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Pickl AI

OCTOBER 9, 2023

However, with the emergence of Machine Learning algorithms, the retail industry has seen a revolutionary shift in demand forecasting capabilities. This technology allows computers to learn from historical data, identify patterns, and make data-driven decisions without explicit programming.

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Google Research AI blog

MARCH 6, 2023

Posted by Yu Zhang, Research Scientist, and James Qin, Software Engineer, Google Research Last November, we announced the 1,000 Languages Initiative , an ambitious commitment to build a machine learning (ML) model that would support the world’s one thousand most-spoken languages, bringing greater inclusion to billions of people around the globe.

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Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. Data forecasting has come a long way since formidable data processing-boosting technologies such as machine learning were introduced.

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Towards AI

MARCH 4, 2023

Fully-Supervised Learning (Non-Neural Network) — powered by — Feature Engineering Supervised learning required input-output examples to train the model. To appreciate what is prompting and to get started, Part 1 discusses 4 major paradigms that have occurred over the past years. Let’s get started !!

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