Dataconomy

AUGUST 31, 2023

Here are some examples of where classification can be used in machine learning: Image recognition : Classification can be used to identify objects within images. Some popular classification algorithms include logistic regression, decision trees, random forests, support vector machines (SVMs), and neural networks.

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Becoming Human

MARCH 16, 2023

In this article, we will explore how AI drug discovery is changing the industry. How drugs are discovered The drug discovery process typically starts with scientists identifying a target in the body, such as a specific protein or hormone, that is involved in the disease. We will look at success stories, AI benefits, and limitations.

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Pickl AI

FEBRUARY 18, 2025

Summary : Sentiment Analysis is a natural language processing technique that interprets and classifies emotions expressed in text. It employs various approaches, including lexicon-based, Machine Learning, and hybrid methods. Sentiment Analysis is a popular task in natural language processing.

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Dataconomy

MAY 25, 2023

In the subsequent sections of this article, we will explore the challenges and limitations associated with artificial intelligence in IoT, as well as the key technologies and techniques driving this convergence. Unsupervised learning Unsupervised learning involves training machine learning models with unlabeled datasets.

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Heartbeat

APRIL 17, 2023

Named entity recognition (NER) is a subtask of natural language processing (NLP) that involves automatically identifying and classifying named entities mentioned in a text. In this article, we will discuss how to perform Named Entity Recognition with SpaCy , a popular Python library for NLP. synonyms).

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How to Learn Machine Learning

DECEMBER 15, 2024

Course Highlights: Detailed exploration of supervised and unsupervised learning In-depth coverage of linear regression, logistic regression, and neural networks Advanced topics including support vector machines and anomaly detection Practical implementation using MATLAB/Octave Insights into machine learning best practices and optimization techniques (..)

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How to Learn Machine Learning

JANUARY 21, 2023

In this article, we’ll explore 7 of the most intriguing AI project ideas for beginners in 2023, providing the perfect opportunity to get your feet wet and jumpstart your AI journey. This can be done using a combination of voice recognition, text-to-speech, and natural language processing to create an interactive experience.

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Pickl AI

AUGUST 1, 2023

Hence, its relevance increases because it helps in extracting useful insights that can drive the decision-making process. In this article, we will explore the concept of applied text mining in Python and how to do text mining in Python. Text mining is also known as text analytics or Natural Language Processing (NLP).

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. GPT4 GPT-4 is the latest and most advanced artificial intelligence system for natural language processing from OpenAI.

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Dataconomy

APRIL 4, 2023

Artificial intelligence (AI) is a broad term that encompasses the ability of computers and machines to perform tasks that normally require human intelligence, such as reasoning, learning, decision-making, and problem-solving. GPT4 GPT-4 is the latest and most advanced artificial intelligence system for natural language processing from OpenAI.

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Mlearning.ai

JULY 4, 2023

One such intriguing aspect is the potential to predict a user’s race based on their tweets, a task that merges the realms of Natural Language Processing (NLP), machine learning, and sociolinguistics.

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Pickl AI

FEBRUARY 7, 2025

This blog explores the difference between Machine Learning and Deep Learning , highlighting their unique characteristics, benefits, and challenges. This article aims to provide a clear comparison, helping you understand when to use Machine Learning and when to opt for Deep Learning based on specific needs and resources.

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IBM Journey to AI blog

JULY 6, 2023

Subcategories of machine learning Some of the most commonly used machine learning algorithms include linear regression , logistic regression, decision tree , Support Vector Machine (SVM) algorithm, Naïve Bayes algorithm and KNN algorithm.

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Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

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Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Please do follow my page if you gained anything useful from the article. Foundations of Statistical Natural Language Processing [M]. Uysal and Gunal, 2014). The architecture of BERT is represented in Figure 14.

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Heartbeat

FEBRUARY 22, 2023

Deep learning for feature extraction, ensemble models, and more Photo by DeepMind on Unsplash The advent of deep learning has been a game-changer in machine learning, paving the way for the creation of complex models capable of feats previously thought impossible.

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Pickl AI

SEPTEMBER 30, 2024

The impact of Machine Learning extends across industries, transforming how businesses operate, compete, and grow. This article explores how ML reshapes business operations, improves decision-making, and fuels growth, highlighting why understanding its impact is crucial for staying ahead in today’s competitive landscape.

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Heartbeat

MARCH 8, 2023

Source: Author Introduction Text classification, which involves categorizing text into specified groups based on its content, is an important natural language processing (NLP) task. This article will look at how R can be used to execute text categorization tasks efficiently.

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Pickl AI

JULY 26, 2023

The concepts of bias and variance in Machine Learning are two crucial aspects in the realm of statistical modelling and machine learning. Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models.

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Heartbeat

FEBRUARY 21, 2024

Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns. This technology has achieved great success in many application areas, especially in image recognition, natural language processing, autonomous vehicles, voice recognition, and many more.

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Pickl AI

NOVEMBER 18, 2024

Summary: The blog provides a comprehensive overview of Machine Learning Models, emphasising their significance in modern technology. It covers types of Machine Learning, key concepts, and essential steps for building effective models. The global Machine Learning market was valued at USD 35.80 billion by 2031 at a CAGR of 34.20%.

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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. By understanding crucial concepts like Machine Learning, Data Mining, and Predictive Modelling, analysts can communicate effectively, collaborate with cross-functional teams, and make informed decisions that drive business success.

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Pickl AI

SEPTEMBER 23, 2024

This article explores how AI and Data Science complement each other, highlighting their combined impact and potential. AI is making a difference in key areas, including automation, language processing, and robotics. Unsupervised Learning techniques such as clustering and dimensionality reduction to discover patterns in data.

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DagsHub

DECEMBER 23, 2024

They are: Based on shallow, simple, and interpretable machine learning models like support vector machines (SVMs), decision trees, or k-nearest neighbors (kNN). You can explore more on this topic in this article by Lilian Weng or this one.   Traditional Active Learning has the following characteristics.

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