Data Science Dojo

MAY 10, 2024

Stage 2: Introduction of neural networks The next step for LLM embeddings was the introduction of neural networks to capture the contextual information within the data. SOMs work to bring down the information into a 2-dimensional map where similar data points form clusters, providing a starting point for advanced embeddings.

Supervised Learning 259

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Dataconomy

APRIL 29, 2025

A keen awareness of where a model lies on the bias-variance spectrum can lead to more informed decisions during the modeling process. Types of errors in machine learning Beyond bias and variance, specific types of errors characterize model performance issues. What is underfitting?

Cross Validation 91

Cross Validation Supervised Learning Machine Learning Machine Learning 91

Dataconomy

MARCH 11, 2025

Linear regression stands out as a foundational technique in statistics and machine learning, providing insights into the relationships between variables. This method enables analysts and practitioners to create predictive models that can inform decision-making across many fields. What is linear regression?

Supervised Learning 91

Supervised Learning Machine Learning Machine Learning Exploratory Data Analysis 91

Data Science Dojo

AUGUST 19, 2024

They dive deep into artificial neural networks, algorithms, and data structures, creating groundbreaking solutions for complex issues. These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential.

AI 364

AI AI Machine Learning Machine Learning 364

Data Science Dojo

APRIL 25, 2025

It takes in data, makes sense of it, and uses that information to plan its next move. Its about creating AI that does not just do, but thinks, learns, and acts on its own. By basing decisions on data and algorithms rather than gut feelings, businesses can reduce the influence of bias in critical systems.

AI 342

AI AI Supervised Learning Algorithm 342

NYU Center for Data Science

SEPTEMBER 13, 2024

The world of multi-view self-supervised learning (SSL) can be loosely grouped into four families of methods: contrastive learning, clustering, distillation/momentum, and redundancy reduction. This work not only brings new theoretical insights but also introduces practical tools to optimize self-supervised models.

Supervised Learning 75

Supervised Learning Clustering Machine Learning Machine Learning 75

Dataconomy

APRIL 24, 2025

Unsupervised learning is a fascinating area within machine learning that uncovers hidden patterns in data without the need for pre-labeled examples. By allowing algorithms to learn autonomously, it opens the door to various innovative applications across different fields. What is unsupervised learning?

Clustering 91

Clustering Machine Learning Machine Learning Algorithm 91

Data Science Dojo

MAY 10, 2024

Stage 2: Introduction of neural networks The next step for LLM embeddings was the introduction of neural networks to capture the contextual information within the data. SOMs work to bring down the information into a 2-dimensional map where similar data points form clusters, providing a starting point for advanced embeddings.

Supervised Learning 195

Supervised Learning Clustering ML ML 195

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.

Supervised Learning 52

Supervised Learning Machine Learning Machine Learning Clustering 52

Dataconomy

MAY 29, 2023

In their quest for effectiveness and well-informed decision-making, businesses continually search for new ways to collect information. QR codes can contain a huge amount of information, such as text, URLs, contact details, and more. In the realm of AI and ML, QR codes find diverse applications across various domains.

Predictive Analytics 208

Predictive Analytics ML ML Analytics 208

Dataconomy

APRIL 29, 2025

Binary classification is a supervised learning method designed to categorize data into one of two possible outcomes. Overview of classification in machine learning Classification serves as a foundational method in machine learning, where algorithms are trained on labeled datasets to make predictions.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Algorithm 91

IBM Data Science in Practice

MARCH 17, 2025

Currently, hand-crafted compression algorithms, often designed for general image data like JPEG2000, are applied. From Data Cubes to Embeddings During the development phase in March, participants will pretrain their encoders using self-supervised learning methods that underpin neural compression and EO foundation models.

AI 130

AI AI Supervised Learning Data Science 130

Towards AI

APRIL 8, 2024

Supervised Learning First, what exactly is supervised learning? It is the most common type of machine learning that you will use. In supervised machine learning, the machine learning algorithm is trained on a labeled dataset. This is where supervised learning would come in handy.

Supervised Learning 105

Supervised Learning Machine Learning Machine Learning Clustering 105

Dataconomy

AUGUST 31, 2023

The classification model learns from the training data, identifying the distinguishing characteristics between each class, enabling it to make informed predictions. Classification in machine learning can be a versatile tool with numerous applications across various industries.

Machine Learning 136

Machine Learning Machine Learning Supervised Learning Algorithm 136

Data Science Dojo

JULY 2, 2024

It is a form of AI that learns, adapts, and improves as it encounters changes, both in data and the environment. Unlike traditional AI, which follows set rules and algorithms and tends to fall apart when faced with obstacles, adaptive AI systems can modify their behavior based on their experiences. What is Adaptive AI?

AI 195

AI AI Algorithm Machine Learning 195

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.

Machine Learning 52

Machine Learning Machine Learning Supervised Learning ML 52

Dataconomy

MAY 4, 2023

From predicting disease outbreaks to identifying complex medical patterns and helping researchers develop targeted therapies, the potential applications of machine learning in healthcare are vast and varied. What is machine learning? From personalized medicine to disease prevention, the possibilities are endless.

Machine Learning 192

Machine Learning Machine Learning Algorithm ML 192

Data Science Dojo

DECEMBER 8, 2023

Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

Supervised Learning 195

Supervised Learning Machine Learning Machine Learning Artificial Intelligence 195

Dataconomy

MARCH 12, 2025

Support Vector Machines (SVM) are a type of supervised learning algorithm designed for classification and regression tasks. In the context of SVMs, it serves as the decision boundary that separates different classes of data, allowing for distinct classifications in supervised learning.

Support Vector Machines 91

Support Vector Machines Decision Trees Supervised Learning Machine Learning 91

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.,

Algorithm 170

Algorithm Machine Learning Machine Learning AI 170

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.

ML 233

ML ML Machine Learning Machine Learning 233

Dataconomy

SEPTEMBER 13, 2023

Imagine a world where computers can’t interpret the visual information around them without a little human assistance. These labels provide crucial context for machine learning models, enabling them to make informed decisions and predictions. That’s where data annotation comes into play.

Machine Learning 195

Machine Learning Machine Learning AI AI 195

Towards AI

JANUARY 11, 2024

In this article, I’ve covered one of the most famous classification and regression algorithms in machine learning, namely the Decision Tree. In contrast, Unsupervised Learning occurs when we lack prior knowledge of the target variable. This often occurs in Cluster Analysis, where we identify clusters without prior information.

Machine Learning 130

Machine Learning Machine Learning Decision Trees Supervised Learning 130

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. For more information, refer to Create a VPC.

AWS 116

AWS K-nearest Neighbors Clustering Algorithm 116

Dataconomy

MAY 25, 2023

On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans. By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions.

Internet of Things 160

Internet of Things Artificial Intelligence Artificial Intelligence AI 160

Dataconomy

APRIL 18, 2023

Additionally, it is crucial to comprehend the fundamental concepts that underlie AI, including neural networks, algorithms, and data structures. AI systems use a combination of algorithms, machine learning techniques, and data analytics to simulate human intelligence. What is artificial intelligence?

Artificial Intelligence 113

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 113

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.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

AWS Machine Learning Blog

JULY 18, 2023

It’s important to take extra precautions to protect your device and sensitive information. The built-in BlazingText algorithm offers optimized implementations of Word2vec and text classification algorithms. The BlazingText algorithm expects a single preprocessed text file with space-separated tokens.

Supervised Learning 98

Supervised Learning Algorithm Natural Language Processing AWS 98

Pickl AI

SEPTEMBER 16, 2024

Summary: This blog highlights ten crucial Machine Learning algorithms to know in 2024, including linear regression, decision trees, and reinforcement learning. Each algorithm is explained with its applications, strengths, and weaknesses, providing valuable insights for practitioners and enthusiasts in the field.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Data Science Dojo

SEPTEMBER 11, 2023

These complex algorithms are the backbone upon which our modern technological advancements rest and which are doing wonders for natural language communication. PaLM 2 stands for “ Progressive and Adaptive Language Model 2 ” and Llama 2 is short for “ Language Learning and Mastery Algorithm 2 ”.

Natural Language Processing 195

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Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? You just want to create and analyze simple maps not to learn algebra all over again.

Machine Learning 102

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 102

Dataconomy

JUNE 5, 2023

By leveraging labeled training data, these models learn the underlying patterns and associations between input features and the desired outcome. This knowledge empowers the models to make informed predictions for new and unseen data, opening up a world of possibilities in diverse domains such as finance, healthcare, retail, and more.

Machine Learning 250

Machine Learning Machine Learning Predictive Analytics Algorithm 250

Google Research AI blog

MARCH 6, 2023

First, there is a lack of scalability with conventional supervised learning approaches. In contrast, self-supervised learning can leverage audio-only data, which is available in much larger quantities across languages. This requires the learning algorithm to be flexible, efficient, and generalizable.

Supervised Learning 140

Supervised Learning AI AI Algorithm 140

Pickl AI

APRIL 13, 2025

Summary: Classifier in Machine Learning involves categorizing data into predefined classes using algorithms like Logistic Regression and Decision Trees. Introduction Machine Learning has revolutionized how we process and analyse data, enabling systems to learn patterns and make predictions.

Machine Learning 52

Machine Learning Machine Learning Decision Trees K-nearest Neighbors 52

Data Science Dojo

SEPTEMBER 1, 2023

Understanding fine-tuning, even if not doing it yourself, aids in informed decision-making. This allows them to learn a wide range of tasks, such as text generation, translation, and question-answering. One common approach is to use supervised learning. This process involves retraining LLMs on new data.

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Towards AI

JULY 22, 2023

We’re educating the computer to learn from data (the equivalent of practice), to make informed predictions (akin to riding the bicycle), and to progressively improve with each iteration. In the context of Machine Learning, data can be anything from images, text, numbers, to anything else that the computer can process and learn from.

ML 97

ML ML Supervised Learning Machine Learning 97

Explosion

APRIL 1, 2017

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. Sometimes the unsupervised algorithm will happen to produce the output you want, but other times it won’t.

Supervised Learning 40

Supervised Learning Machine Learning Machine Learning Algorithm 40