Flipboard

JUNE 17, 2025

Essential Skills for Solo AI Business TL;DR Key Takeaways : A strong understanding of AI fundamentals, including algorithms, neural networks, and natural language processing, is essential for creating effective AI solutions and making informed decisions.

Machine Learning 67

Machine Learning Machine Learning AI AI 67

ODSC - Open Data Science

JULY 14, 2025

She’s the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the Routledge book, Massive Graph Analytics , and the Bloomsbury book, AI on Trial. Suman Debnath, Principal AI/ML Advocate at Amazon Web Services Suman Debnath is a Principal Machine Learning Advocate at Amazon Web Services.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

ODSC - Open Data Science

JULY 3, 2025

She’s the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the Routledge book, Massive Graph Analytics , and the Bloomsbury book, AI on Trial. Suman Debnath, Principal AI/ML Advocate at Amazon Web Services Suman Debnath is a Principal Machine Learning Advocate at Amazon Web Services.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Flipboard

APRIL 4, 2025

Large language models (LLMs) can be used to perform natural language processing (NLP) tasks ranging from simple dialogues and information retrieval tasks, to more complex reasoning tasks such as summarization and decision-making. This leads to responses that are untruthful, toxic, or simply not helpful to the user.

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AI AI Algorithm Artificial Intelligence 143

How to Learn Machine Learning

MAY 14, 2025

Y2Mate is the fastest YouTube downloader tool available, working like a well-optimized algorithm to convert and download videos in record time! The Best YouTube Downloader for ML Enthusiasts Before we dive into the how-to, let me introduce you to an awesome tool that’s about to become your new best friend in data collection.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Dataconomy

MARCH 28, 2025

The backpropagation algorithm is a cornerstone of modern machine learning, enabling neural networks to learn from data effectively. Understanding how backpropagation operates not only reveals the intricacies of neural networks but also illuminates the underlying processes that power AI advancements today.

Algorithm 103

Algorithm Machine Learning Machine Learning Natural Language Processing 103

How to Learn Machine Learning

MAY 2, 2025

Learn more from the MLflow with Azure ML documentation. Automated Machine Learning (AutoML) : This feature automates time-consuming tasks like algorithm selection, hyperparameter tuning, and feature engineering. Simply prepare your data, define your target variable, and let AutoML explore various algorithms and hyperparameters.

Azure 52

Azure Machine Learning Machine Learning Data Science 52

Dataconomy

MAY 12, 2025

A validation set is a critical element in the machine learning process, particularly for those working within the realms of supervised learning. Its applications range from image recognition to natural language processing, highlighting the significance of building robust and adaptable models.

Supervised Learning 91

Supervised Learning Machine Learning Machine Learning Natural Language Processing 91

Dataconomy

MARCH 13, 2025

These sophisticated algorithms facilitate a deeper understanding of data, enabling applications from image recognition to natural language processing. What is deep learning? Deep learning is a subset of artificial intelligence that utilizes neural networks to process complex data and generate predictions.

Deep Learning 91

Deep Learning Deep Learning Natural Language Processing Machine Learning 91

Dataconomy

MARCH 6, 2025

Algorithms play a crucial role in our everyday lives, often operating behind the scenes to enhance our experiences in the digital world. From the way search engines deliver results to how personal assistants predict our needs, algorithms are the foundational elements that shape modern technology. What is an algorithm?

Algorithm 91

Algorithm Machine Learning Machine Learning Deep Learning 91

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. This ground truth data is necessary to train the supervised learning model for a multiclass classification use case.

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How to Learn Machine Learning

MAY 7, 2025

Machine Learning, explained in simple words, is a subfield of artificial intelligence which focuses on the development of algorithms and statistical models which activate computers to learn and make predictions or decisions without being explicitly programmed. It creates realistic images, music, or even human-like text.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

Dataconomy

MARCH 26, 2025

AI data labeling refers to the process of identifying and tagging data to train supervised learning models effectively. This critical step ensures that machine learning algorithms can recognize patterns and make predictions with greater accuracy. What is AI data labeling?

Machine Learning 91

Machine Learning Machine Learning AI AI 91

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 446

Supervised Learning Machine Learning Machine Learning Artificial Intelligence 446

Data Science Dojo

MAY 27, 2024

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.

K-nearest Neighbors 338

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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.

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Data Science Dojo

MAY 10, 2024

Hence, acting as a translator it converts human language into a machine-readable form. These embeddings when particularly used for natural language processing (NLP) tasks are also referred to as LLM embeddings. The two main approaches of interest for embeddings include unsupervised and supervised learning.

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Data Science Dojo

JUNE 1, 2023

Machine Learning for Absolute Beginners by Kirill Eremenko and Hadelin de Ponteves This is another beginner-level course that teaches you the basics of machine learning using Python. The course covers topics such as supervised learning, unsupervised learning, and reinforcement learning.

Machine Learning 195

Machine Learning Machine Learning Natural Language Processing Deep Learning 195

Data Science Dojo

SEPTEMBER 11, 2023

Language models, a recent advanced technology that is blooming more and more as the days go by. These complex algorithms are the backbone upon which our modern technological advancements rest and which are doing wonders for natural language communication. These are more than just names; they are the cutting edge of NLP.

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Dataconomy

MAY 29, 2023

These algorithms allow AI systems to recognize patterns, forecast outcomes, and adjust to new situations. The applications of AI span diverse domains, including natural language processing, computer vision, robotics, expert systems, and machine learning.

Predictive Analytics 208

Predictive Analytics ML ML Analytics 208

Data Science Dojo

MAY 10, 2024

Hence, acting as a translator it converts human language into a machine-readable form. These embeddings when particularly used for natural language processing (NLP) tasks are also referred to as LLM embeddings. The two main approaches of interest for embeddings include unsupervised and supervised learning.

Supervised Learning 195

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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

Smart Data Collective

JUNE 4, 2021

Data scientists use algorithms for creating data models. Whereas in machine learning, the algorithm understands the data and creates the logic. Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning.

Data Science 132

Data Science Exploratory Data Analysis Machine Learning Machine Learning 132

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. This type of problem is more challenging because the model needs to learn more complex relationships between the input features and the multiple classes.

Machine Learning 136

Machine Learning Machine Learning Supervised Learning Algorithm 136

Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. Various algorithms are employed in image captioning, including: 1. These algorithms can learn and extract intricate features from input images by using convolutional layers.

Natural Language Processing 52

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Dataconomy

APRIL 18, 2023

The basics of artificial intelligence include understanding the various subfields of AI, such as machine learning, natural language processing, computer vision, and robotics. Additionally, it is crucial to comprehend the fundamental concepts that underlie AI, including neural networks, algorithms, and data structures.

Artificial Intelligence 113

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Data Science Dojo

JUNE 11, 2024

These include image recognition, natural language processing, autonomous vehicles, financial services, healthcare, recommender systems, gaming and entertainment, and speech recognition. They are capable of learning and improving over time as they are exposed to more data.

Natural Language Processing 195

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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? Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

AWS Machine Learning Blog

JULY 18, 2023

The built-in BlazingText algorithm offers optimized implementations of Word2vec and text classification algorithms. Word2vec is useful for various natural language processing (NLP) tasks, such as sentiment analysis, named entity recognition, and machine translation. We can see our dataset is balanced.

Supervised Learning 98

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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.

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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

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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Machine Learning Machine Learning ML ML 109

Dataconomy

MARCH 14, 2023

In recent years, natural language processing and conversational AI have gained significant attention as technologies that are transforming the way we interact with machines and each other. Moreover, the model training process is capable of adapting to new languages and data effectively.

Natural Language Processing 70

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Data Science Dojo

JULY 4, 2024

Subjectivity Handling : Since human feedback can capture nuances and subjective assessments that are challenging to define algorithmically, RLHF is particularly effective for tasks that require a deep understanding of context and user intent. Applications include chatbots, image generation, music creation, and voice assistants.

AI 195

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Towards AI

JULY 23, 2023

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train.

Natural Language Processing 98

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Dataconomy

MARCH 27, 2023

Some of the ways in which ML can be used in process automation include the following: Predictive analytics:  ML algorithms can be used to predict future outcomes based on historical data, enabling organizations to make better decisions. What is machine learning (ML)?

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ML ML Machine Learning Machine Learning 133

Dataconomy

AUGUST 21, 2023

At its core, a Large Language Model (LLM) is a sophisticated machine learning entity adept at executing a myriad of natural language processing (NLP) activities. This includes tasks like text generation, classification, engaging in dialogue, and even translating text across languages. What is LLM in AI?

Natural Language Processing 91

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