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.

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Dataversity

DECEMBER 16, 2022

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements.

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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

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.

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Data Science Dojo

SEPTEMBER 11, 2023

From virtual assistants like Siri and Alexa to personalized recommendations on streaming platforms, chatbots, and language translation services, language models surely are the engines that power it all. If the goal is a creative and informative content generation, Llama 2 is the ideal choice.

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Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. That is, is giving supervision to adjust via.

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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.

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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 excel in processing sequential data for tasks such as speech recognition, natural language processing, and time series prediction.

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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.

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IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. Semi-supervised learning The fifth type of machine learning technique offers a combination between supervised and unsupervised learning.

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Pickl AI

OCTOBER 21, 2024

This innovative approach is transforming applications in computer vision, Natural Language Processing, healthcare, and more. Introduction Zero-Shot Learning (ZSL) is revolutionising Artificial Intelligence by enabling models to classify new categories without prior training data.

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Dataconomy

APRIL 18, 2023

Understanding the basics of artificial intelligence Artificial intelligence is an interdisciplinary field of study that involves creating intelligent machines that can perform tasks that typically require human-like cognitive abilities such as learning, reasoning, and problem-solving.

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Pickl AI

AUGUST 14, 2024

They consist of interconnected nodes that learn complex patterns in data. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

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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?

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Mlearning.ai

AUGUST 4, 2023

Fine-tuning large language models (LLMs) has become a powerful technique for achieving impressive performance in various natural language processing tasks. Additionally, few-shot learning requires maintaining a large context window, which can be resource-intensive and impractical for memory-constrained environments.

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Pickl AI

SEPTEMBER 16, 2024

This section will explore the top 10 Machine Learning algorithms that you should know in 2024. Linear Regression Linear regression is one of the simplest and most widely used algorithms in Machine Learning. It is a supervised learning algorithm that predicts a continuous target variable based on one or more predictor variables.

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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.

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Dataconomy

MAY 29, 2023

The applications of AI span diverse domains, including natural language processing, computer vision, robotics, expert systems, and machine learning. Some of the methods used in ML include supervised learning, unsupervised learning, reinforcement learning, and deep learning.

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Towards AI

MARCH 4, 2023

Fully-Supervised Learning (Non-Neural Network) — powered by — Feature Engineering Supervised learning required input-output examples to train the model. To appreciate what is prompting and to get started, Part 1 discusses 4 major paradigms that have occurred over the past years. Let’s get started !!

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Data Science Dojo

AUGUST 19, 2024

These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential. Supervised learning: This involves training a model on a labeled dataset, where each data point has a corresponding output or target variable.

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. Types of ML There are three main types of machine learning: Supervised learning: In supervised learning, the algorithm is trained on labeled data.

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. Types of ML There are three main types of machine learning: Supervised learning: In supervised learning, the algorithm is trained on labeled data.

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Pickl AI

JULY 12, 2024

Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data. There are three main types of Machine Learning: supervised learning, unsupervised learning, and reinforcement learning.

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AWS Machine Learning Blog

JULY 18, 2023

Word2vec is useful for various natural language processing (NLP) tasks, such as sentiment analysis, named entity recognition, and machine translation. Set the learning mode hyperparameter to supervised. BlazingText has both unsupervised and supervised learning modes. Start training the model.

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Becoming Human

MAY 11, 2023

Empowering Startups and Entrepreneurs | InvestBegin.com | investbegin The success of ChatGPT can be attributed to several key factors, including advancements in machine learning, natural language processing, and big data. NLP is a field of AI that focuses on enabling computers to understand and process human language.

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Becoming Human

MAY 9, 2024

Artificial intelligence, machine learning, natural language processing, and other related technologies are paving the way for a smarter “everything.” As a result, we can automate manual processes, improve risk management, comply with regulations, and maintain data consistency.

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Women in Big Data

DECEMBER 13, 2023

The Bay Area Chapter of Women in Big Data (WiBD) hosted its second successful episode on the NLP (Natural Language Processing), Tools, Technologies and Career opportunities. Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that helps computers understand, interpret and manipulate human language.

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How to Learn Machine Learning

MAY 14, 2023

Acquiring Essential Machine Learning Knowledge Once you have a strong foundation in mathematics and programming, it’s time to dive into the world of machine learning. Additionally, you should familiarize yourself with essential machine learning concepts such as feature engineering, model evaluation, and hyperparameter tuning.

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Pickl AI

SEPTEMBER 16, 2024

DBNs learn to represent data by modelling complex distributions through a hierarchical structure. They extract features from data by capturing higher-level abstractions, making them suitable for complex tasks like image recognition and natural language processing. However, their approach differs.

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Towards AI

FEBRUARY 20, 2024

Types of Machine Learning There are three main categories of Machine Learning, Supervised learning, Unsupervised learning, and Reinforcement learning. Supervised learning: This involves learning from labeled data, where each data point has a known outcome.

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Pickl AI

OCTOBER 10, 2024

Foundation Models Foundation models are large-scale Machine Learning models pre-trained on vast datasets using self-supervised learning techniques. How They Work These models use techniques like computer vision for image processing alongside Natural Language Processing (NLP) for text analysis.

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Pickl AI

NOVEMBER 13, 2024

Text Augmentation In Natural Language Processing (NLP), text augmentation plays a crucial role in enhancing the diversity of text data. Natural Language Processing (NLP) In NLP, Data Augmentation helps improve text classification, sentiment analysis, and machine translation.

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Pickl AI

SEPTEMBER 17, 2024

Deep Learning (DL) is a more advanced technique within Machine Learning that uses artificial neural networks with multiple layers to learn from and make predictions based on data. Explain The Concept of Supervised and Unsupervised Learning. What Is the Role of Ensemble Methods in Machine Learning?

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ODSC - Open Data Science

AUGUST 29, 2023

Natural Language Processing Engineer Natural Language Processing Engineers who specialize in prompt engineering are linguistic architects when it comes to AI communication. As AI models become more sophisticated and versatile, the demand for tailored, context-aware interactions grows.

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Smart Data Collective

JUNE 4, 2021

Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Machine learning is broadly classified into three types – Supervised. In supervised learning, a variable is predicted. Semi-Supervised Learning.

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Pickl AI

NOVEMBER 18, 2024

The main types are supervised, unsupervised, and reinforcement learning, each with its techniques and applications. Supervised Learning In Supervised Learning , the algorithm learns from labelled data, where the input data is paired with the correct output. spam email detection) and regression (e.g.,

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Pickl AI

MAY 29, 2024

Multi-Task Learning Deep Learning is a towering pillar in the vast landscape of artificial intelligence, revolutionising various domains with remarkable capabilities. Deep Learning algorithms have become integral to modern technology, from image recognition to Natural Language Processing.

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