How to Learn Machine Learning

MAY 2, 2025

Welcome to this comprehensive guide on Azure Machine Learning , Microsoft’s powerful cloud-based platform that’s revolutionizing how organizations build, deploy, and manage machine learning models. This is where Azure Machine Learning shines by democratizing access to advanced AI capabilities.

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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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How to Learn Machine Learning

MARCH 25, 2025

Understanding Supervised vs Unsupervised Learning: A Comparative Overview Introduction Hello dear readers, hope you’re doing just fine! (Or Or even better than that) Machine learning has transformed the way businesses operate by automating processes, analyzing data patterns, and improving decision-making.

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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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Dataconomy

JUNE 16, 2025

Definition and characteristics of structured data Structured data is typically characterized by its organization within fixed fields in databases. Machine learning Structured data is crucial in machine learning applications. It provides clear and organized datasets essential for training supervised learning models.

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Dataconomy

APRIL 14, 2025

Instance-based learning (IBL) is a fascinating approach within the realm of machine learning that emphasizes the importance of individual data points rather than abstracting information into generalized models. This method allows systems to utilize specific historical examples to inform predictions about new instances.

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Dataconomy

MARCH 10, 2025

Ground truth is a fundamental concept in machine learning, representing the accurate, labeled data that serves as a crucial reference point for training and validating predictive models. What is ground truth in machine learning?

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Hacker News

JUNE 14, 2025

I believe current Q-learning algorithms are not readily scalable, at least to long-horizon problems that require more than (say) 100 semantic decision steps. My definition of scalability here is the ability to solve more challenging, longer-horizon problems with more data (of sufficient coverage), compute, and time. Let me clarify.

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The MLOps Blog

MARCH 21, 2023

Moving across the typical machine learning lifecycle can be a nightmare. Machine learning platforms are increasingly looking to be the “fix” to successfully consolidate all the components of MLOps from development to production. What is a machine learning platform? That’s where this guide comes in!

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Dataconomy

MARCH 12, 2025

Support Vector Machines (SVM) are a cornerstone of machine learning, providing powerful techniques for classifying and predicting outcomes in complex datasets. What are Support Vector Machines (SVM)? Support Vector Machines (SVM) are a type of supervised learning algorithm designed for classification and regression tasks.

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Towards AI

FEBRUARY 7, 2025

How do you tell the Machine Learning models the meaning of a particular word, especially when they are quantitatively intelligent and lexically challenged? This definitely estimates the spectrum of usage of text annotations to cater to the demands of the AI revolution across various industries. Behind the Medium paywall?

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Dataconomy

MARCH 13, 2025

The functionality of deep learning Deep learning relies heavily on the architecture of neural networks, which consist of interconnected layers that process information similarly to the human brain. Definition of neural networks Neural networks are designed to recognize patterns in data.

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Dataconomy

MARCH 6, 2025

Definition and characteristics of algorithms Algorithms are characterized by their systematic procedures. Machine learning as an algorithm example Machine learning encompasses a variety of algorithms that learn from data and improve over time.

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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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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

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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 Machine Learning?

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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. Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. What is machine learning (ML)?

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Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. This concept, pivotal in understanding data structures and communication systems, plays a significant role in Machine Learning.

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Mlearning.ai

JULY 6, 2023

NOTES, DEEP LEARNING, REMOTE SENSING, ADVANCED METHODS, SELF-SUPERVISED LEARNING A note of the paper I have read Photo by Kelly Sikkema on Unsplash Hi everyone, In today’s story, I would share notes I took from 32 pages of Wang et al., Taxonomy of the self-supervised learning Wang et al. 2022’s paper.

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Pickl AI

SEPTEMBER 17, 2024

It enhances visualisation, improves model performance, and mitigates overfitting, making it easier to interpret data and extract meaningful insights in Machine Learning and statistics. Dimensionality reduction in Machine Learning enhances model performance and improves data visualisation by focusing on the most significant dimensions.

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AssemblyAI

MAY 17, 2023

The below animation demonstrates this process: 0:00 / 1× Since the Language Model is, by definition, a probability distribution over word sequences, we generate text by simply recursively asking for the most likely next word given all of our previous words. Yes, it really is that simple. Can we do better?

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Explosion

APRIL 1, 2017

We wrote this post while working on Prodigy , our new annotation tool for radically efficient machine teaching. Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. It’s a cloud-free, downloadable tool and comes with powerful active learning models.

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Defined.ai blog

SEPTEMBER 14, 2022

Teaching Through Data The purpose of annotating data is to tell machine learning models exactly what we want them to know. Teaching a machine to learn through annotation can be likened to teaching a toddler shapes and colors using flashcards, where the annotations are the flashcards and annotators are the teacher.

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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. Some of them may even be deemed outdated by now.

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AWS Machine Learning Blog

MARCH 27, 2025

In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. When you evaluate a case, evaluate the definitions in order and label the case with the first definition that fits.

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Applied Data Science

MARCH 11, 2022

Our internal agents are playing games until they learn how to cooperate and trick us into believing we are an individual. Gamification There are many definitions for what a game is. How can you tell which features are the most appropriate, before giving them to a machine learning model? Many AI researchers think there is.

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Mlearning.ai

APRIL 13, 2023

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

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DagsHub

NOVEMBER 18, 2024

Image labeling and annotation are the foundational steps in accurately labeling the image data and developing machine learning (ML) models for the computer vision task. In this article, you will learn about the importance of image annotation and what you should know for annotating image files for machine learning at scale.

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Explosion

MAY 17, 2023

That’s definitely new. Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find.

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ML @ CMU

APRIL 23, 2025

Interpretability and Explainable AI Learning on Graphs and Other Geometries & Topologies Learning Theory Neurosymbolic & Hybrid AI Systems (Physics-Informed, Logic & Formal Reasoning, etc.) Optimization Other Topics in Machine Learning (i.e., Lee Other Topics In Machine Learning (I.e.,

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Towards AI

FEBRUARY 15, 2024

I definitely recommend watching this one for all learners out here! Ramcharan12345 is looking to collaborate with AI devs who can leverage spaCy for NLP, utilize scikit-learn for supervised learning on historical data for symptom mapping, and implement TensorFlow/Keras for neural network-based risk prediction.

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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. Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. What is machine learning (ML)?

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Data Science 101

OCTOBER 31, 2019

A definition from the book ‘Data Mining: Practical Machine Learning Tools and Techniques’, written by, Ian Witten and Eibe Frank describes Data mining as follows: “ Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Classification. Regression.

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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. Introduction Artificial Intelligence (AI) transforms industries by enabling machines to mimic human intelligence.

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Snorkel AI

JULY 5, 2023

I lead product marketing efforts for TensorFlow and multiple open-source and machine learning initiatives at Google. The first of these questions that we often see coming from our community is that in an age of big data, is the sheer volume of available data the primary determinant of machine learning success?

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Snorkel AI

JULY 5, 2023

I lead product marketing efforts for TensorFlow and multiple open-source and machine learning initiatives at Google. The first of these questions that we often see coming from our community is that in an age of big data, is the sheer volume of available data the primary determinant of machine learning success?

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Snorkel AI

JULY 5, 2023

I lead product marketing efforts for TensorFlow and multiple open-source and machine learning initiatives at Google. The first of these questions that we often see coming from our community is that in an age of big data, is the sheer volume of available data the primary determinant of machine learning success?

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