Dataconomy

MARCH 28, 2025

The backpropagation algorithm is a cornerstone of modern machine learning, enabling neural networks to learn from data effectively. What is backpropagation algorithm? The backpropagation algorithm is a method used to train neural networks by optimizing the weights based on the error of predictions.

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How to Learn Machine Learning

JULY 6, 2025

In today’s data-driven world, machine learning fuels creativity across industries-from healthcare and finance to e-commerce and entertainment. For many fulfilling roles in data science and analytics, understanding the core machine learning algorithms can be a bit daunting with no examples to rely on.

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Data Science Dojo

MAY 27, 2024

In the recent discussion and advancements surrounding artificial intelligence, there’s a notable dialogue between discriminative and generative AI approaches. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world. What is Generative AI?

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Dataconomy

APRIL 29, 2025

Human-in-the-loop (HITL) machine learning is a transformative approach reshaping how machine learning models learn and improve. Human-in-the-loop machine learning is a methodology that emphasizes the critical role of human feedback in the machine learning lifecycle.

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Dataconomy

APRIL 18, 2023

With the rise of AI-generated art and AI-powered chatbots like ChatGPT, it’s clear that artificial intelligence has become a ubiquitous part of our daily lives. But amidst all the hype, it’s worth asking ourselves: do we really understand the basics of artificial intelligence? What is artificial intelligence?

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Dataconomy

MAY 29, 2023

This article examines the important connection between QR codes and the domains of artificial intelligence (AI) and machine learning (ML), as well as how it affects the development of predictive analytics. So let’s start with the understanding of QR Codes, Artificial intelligence, and Machine Learning.

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Dataconomy

MARCH 13, 2023

How to create an artificial intelligence? The creation of artificial intelligence (AI) has long been a dream of scientists, engineers, and innovators. With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless.

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Data Science Dojo

APRIL 25, 2025

Agentic AI is a type of artificial intelligence (AI) that does not just follow rules but acts like an intelligent agent. By basing decisions on data and algorithms rather than gut feelings, businesses can reduce the influence of bias in critical systems. Ready to explore more? Lets dive in. What is Agentic AI?

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Dataconomy

NOVEMBER 1, 2018

A clever algorithm that has digested seven decades’ worth of articles in China’s state-run media is now ready to predict its future policies. Supervised learning — the most developed form of Machine. The research design of this “crystal ball” can also be applied to tackling a variety of other problems.

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Dataconomy

MAY 25, 2023

The integration of artificial intelligence in Internet of Things introduces new dimensions of efficiency, automation, and intelligence to our daily lives. Simultaneously, artificial intelligence has revolutionized the way machines learn, reason, and make decisions.

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Dataconomy

FEBRUARY 26, 2025

This capability bridges various disciplines, leveraging techniques from statistics, machine learning, and artificial intelligence. Artificial intelligence (AI): It enables machines to learn from data, improving decision-making and automation.

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Dataconomy

SEPTEMBER 13, 2023

As businesses gather increasingly deep insights into their customers, artificial intelligence (AI) emerges as a powerful ally to turn this data into actionable strategies. These tasks are indispensable, as algorithms heavily rely on pattern recognition to make informed decisions. Featured image credit: Alex Knight/Unsplash

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Data Science Dojo

DECEMBER 8, 2023

Counting Shots, Making Strides: Zero, One and Few-Shot Learning Unleashed In the dynamic field of artificial intelligence, traditional machine learning, reliant on extensive labeled datasets, has given way to transformative learning paradigms. Welcome to the frontier of machine learning innovation!

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Data Science Dojo

AUGUST 19, 2024

Business Benefits: Organizations are recognizing the value of AI and data science in improving decision-making, enhancing customer experiences, and gaining a competitive edge An AI research scientist acts as a visionary, bridging the gap between human intelligence and machine capabilities. Privacy: Protecting user privacy and data security.

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Pickl AI

APRIL 3, 2023

Accordingly, Machine Learning allows computers to learn and act like humans by providing data. Apparently, ML algorithms ensure to train of the data enabling the new data input to make compelling predictions and deliver accurate results. Therefore, Supervised Learning vs Unsupervised Learning is part of Machine Learning.

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Dataconomy

MARCH 5, 2025

DeepMind is at the forefront of artificial intelligence research, merging cutting-edge technology with innovative applications. is dedicated to creating systems that can learn and adapt, a fundamental step toward achieving General-Purpose Artificial Intelligence (AGI). This ambitious division of Alphabet, Inc.

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Dataconomy

APRIL 14, 2025

By leveraging the similarity between instances, IBL provides a unique perspective on how algorithms can adapt and learn from previously encountered data. What is instance-based learning (IBL)? Unsupervised learning: Focuses on extracting patterns from data without pre-labeled responses, identifying inherent structures.

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Dataconomy

MARCH 10, 2025

The perceptron is a fundamental concept in the field of artificial intelligence, particularly in neural networks. It serves as the backbone for understanding how machines can learn from data to make decisions. Recent developments in machine learning platforms and frameworks emphasize their role in foundational AI concepts.

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Smart Data Collective

JUNE 7, 2019

Machine learning is playing a very important role in improving the functionality of task management applications. Appreciating the Machine Learning Technology Behind Modern Task Management Software. Although there are many types of learning, Michalski defined the two most common types of learning: Supervised Learning.

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How to Learn Machine Learning

MARCH 25, 2025

At the core of machine learning, two primary learning techniques drive these innovations. These are known as supervised learning and unsupervised learning. Supervised learning and unsupervised learning differ in how they process data and extract insights. The data is raw and unstructured.

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Dataconomy

MARCH 13, 2025

Deep learning is transforming the landscape of artificial intelligence (AI) by mimicking the way humans learn and interpret complex data. These sophisticated algorithms facilitate a deeper understanding of data, enabling applications from image recognition to natural language processing. What is deep learning?

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Dataconomy

MAY 22, 2023

By dividing the workload and data across multiple nodes, distributed learning enables parallel processing, leading to faster and more efficient training of machine learning models. There are various types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

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Dataconomy

MARCH 12, 2025

Machine teaching is redefining how we interact with artificial intelligence (AI) and machine learning (ML). As industries increasingly adopt AI solutions, professionals without a technical background can now step into the realm of machine learning, leveraging powerful algorithms to automate tasks and improve decision-making.

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Pickl AI

FEBRUARY 17, 2025

These figures underscore the pressing need for awareness and solutions regarding the challenges faced by Machine Learning professionals. Key Takeaways Data quality is crucial; poor data leads to unreliable Machine Learning models. Algorithmic bias can result in unfair outcomes, necessitating careful management.

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Dataconomy

OCTOBER 5, 2023

Artificial intelligence, one of the most talked about topics in today’s technology world, has played a huge role in bringing many things into our lives, especially in the last five years. But does that mean artificial intelligence is perfect? The next critical step is model selection.

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Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. While AI aims to replicate human intelligence across various domains, ML focuses on learning from data to improve performance. What is Artificial Intelligence?

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Data Science Dojo

JULY 2, 2024

It is a step ahead within the realm of artificial intelligence (AI). It is a form of AI that learns, adapts, and improves as it encounters changes, both in data and the environment. Some key characteristics that make AI adaptive are: Ability to Learn Continuously The AI system can process and analyze new information.

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Pickl AI

SEPTEMBER 17, 2024

Summary: This blog covers 15 crucial artificial intelligence interview questions, ranging from fundamental concepts to advanced techniques. Introduction Artificial Intelligence (AI) has become an increasingly important field in recent years, with a growing demand for skilled professionals who can navigate its complexities.

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Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. It equips you to build and deploy intelligent systems confidently and efficiently.

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IBM Journey to AI blog

DECEMBER 20, 2023

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. What is machine learning? ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions.

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Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. Machine learning is the science of building models automatically. It is a branch of artificial intelligence. Whereas in machine learning, the algorithm understands the data and creates the logic. In supervised learning, a variable is predicted.

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Towards AI

SEPTEMBER 3, 2024

Let’s discuss two popular ML algorithms, KNNs and K-Means. They are both ML Algorithms, and we’ll explore them more in detail in a bit. K-Nearest Neighbors (KNN) is a supervised ML algorithm for classification and regression. This member-only story is on us. Upgrade to access all of Medium.

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AWS Machine Learning Blog

FEBRUARY 13, 2025

However, to demonstrate how this system works, we use an algorithm designed to reduce the dimensionality of the embeddings, t-distributed Stochastic Neighbor Embedding (t-SNE) , so that we can view them in two dimensions. This is the k-nearest neighbor (k-NN) algorithm. The following figure illustrates how this works.

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Pickl AI

SEPTEMBER 23, 2024

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. Introduction Artificial Intelligence (AI) and Data Science are revolutionising how we analyse data, make decisions, and solve complex problems.

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IBM Journey to AI blog

DECEMBER 19, 2023

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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Pickl AI

OCTOBER 20, 2024

Summary: Learning Artificial Intelligence involves mastering Python programming, understanding Machine Learning principles, and engaging in practical projects. Introduction Artificial Intelligence (AI) is transforming industries worldwide, with applications in healthcare, finance, and technology.

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ODSC - Open Data Science

JULY 4, 2025

Machine Learning Covering modern ML topics — including ensemble algorithms, feature engineering, AutoML, real-time, and edge deployments — this track emphasizes explainability, bias mitigation, and domain-specific case studies. Learn how to build resilient, production-grade AI systems end-to-end.

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