Data Science Dojo

JULY 4, 2023

Generative AI harnesses deep learning algorithms to generate human-like data in response to user input. This technology finds applications in NLP, computer vision, autonomous driving, robotics, and more. Back to basics: What is Generative AI? It goes beyond traditional programming, empowering machines with creativity and curiosity.

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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. This necessitates a flexible, efficient, and generalizable learning algorithm. What is natural language processing (NLP)?

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Dataconomy

MAY 29, 2023

Explanation of AI and ML Artificial Intelligence (AI) refers to a field within computer science dedicated to the creation of intelligent machines, capable of executing tasks typically requiring human intelligence. These algorithms allow AI systems to recognize patterns, forecast outcomes, and adjust to new situations.

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DrivenData Labs

MAY 18, 2023

I work on machine learning for natural language processing, and I’m particularly interested in few-shot learning, lifelong learning, and societal and health applications such as abuse detection, misinformation, mental ill-health detection, and language assessment. How did you get started in data science?

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Dataconomy

APRIL 3, 2023

Artificial intelligence, commonly referred to as AI , is the field of computer science that focuses on the development of intelligent machines that can perform tasks that would typically require human intervention. Unsupervised learning: In unsupervised learning, the algorithm is trained on unlabeled data.

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Dataconomy

APRIL 3, 2023

Artificial intelligence, commonly referred to as AI , is the field of computer science that focuses on the development of intelligent machines that can perform tasks that would typically require human intervention. Unsupervised learning: In unsupervised learning, the algorithm is trained on unlabeled data.

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Pickl AI

APRIL 25, 2023

There are various Machine Learning algorithms including regression and classification algorithms. Consequently, ML algorithms can be further divided into two categories, supervised and unsupervised. Supervised algorithms require you to train datasets for both the input data and the desired output. What is NLP?

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Dataconomy

APRIL 12, 2023

By incorporating insights from cognitive science, AI is becoming more advanced and capable, with the potential to transform many aspects of our lives. Artificial intelligence, or AI, is a field of computer science and engineering that focuses on creating machines and systems that can perform tasks that typically require human intelligence.

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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.

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ODSC - Open Data Science

DECEMBER 19, 2023

Data scientists with a PhD or a master’s degree in computer science or a related field can earn more than $150,000 per year. They use their knowledge of machine learning algorithms, programming languages, and data science tools to build models that can be used to automate tasks and make predictions.

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Smart Data Collective

MARCH 27, 2023

Data Scientists and Analysts use various tools such as machine learning algorithms, statistical modeling, natural language processing (NLP), and predictive analytics to identify trends, uncover opportunities for improvement, and make better decisions. as this will set you apart from other applicants.

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NYU Center for Data Science

JULY 18, 2023

MaD & MaD+ The Math and Data (MaD) group is a collaboration between CDS and the NYU Courant Institute of Mathematical Sciences. And how can we best use insights from natural intelligence to develop new, more powerful machine intelligence technologies that more fruitfully interact with us?”

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Heartbeat

AUGUST 23, 2023

But what if there was a technique to quickly and accurately solve this language puzzle? Enter Natural Language Processing (NLP) and its transformational power. But what if there was a way to unravel this language puzzle swiftly and accurately? Algorithms can automatically detect and extract key items.

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Pickl AI

JULY 12, 2024

Their interactive nature makes them suitable for experimenting with AI algorithms and analysing data. Here are a few of the key concepts that you should know: Machine Learning (ML) This is a type of AI that allows computers to learn without being explicitly programmed.

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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. 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. What is machine learning?

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Dataconomy

MARCH 23, 2023

Artificial intelligence is a branch of computer science that involves the development of intelligent machines that can perform tasks that typically require human intelligence. AI technologies include machine learning, natural language processing, computer vision, and robotics, among others.

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Pickl AI

SEPTEMBER 26, 2024

Summary: This blog provides a comprehensive guide to the top 15 Natural Language Processing (NLP) interview questions and answers. Introduction Natural Language Processing (NLP) is a rapidly advancing field that sits at the intersection of linguistics, computer science, and artificial intelligence.

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Dataconomy

MARCH 13, 2023

With advances in machine learning, deep learning, and natural language processing, the possibilities of what we can create with AI are limitless. However, the process of creating AI can seem daunting to those who are unfamiliar with the technicalities involved. Train and evaluate the AI models for accuracy and efficiency.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. Computer science, math, statistics, programming, and software development are all skills required in NLP projects.

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Pickl AI

JANUARY 29, 2024

Summary: In the tech landscape of 2024, the distinctions between Data Science and Machine Learning are pivotal. Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation. billion by 2029.

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Pickl AI

SEPTEMBER 18, 2024

Summary: Machine Learning Engineer design algorithms and models to enable systems to learn from data. A Machine Learning Engineer plays a crucial role in this landscape, designing and implementing algorithms that drive innovation and efficiency. In finance, they build models for risk assessment or algorithmic trading.

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Towards AI

AUGUST 16, 2023

AI engineering is the discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts, which combines the principles of systems engineering, software engineering, and computer science to create AI systems.

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How to Learn Machine Learning

MAY 14, 2023

Mathematics is crucial because machine learning algorithms are built on concepts such as linear algebra, calculus, probability, and statistics. Familiarity with these subjects will enable you to understand and implement machine learning algorithms more effectively.

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NYU Center for Data Science

DECEMBER 12, 2023

In an era where algorithms determine everything from creditworthiness to carceral sentencing, the imperative for responsible innovation has never been more urgent. Andrew Bell and Lucius Bynum: Challenging Algorithmic Boundaries Andrew Bell’s exploration of algorithmic fairness sets a foundation for the responsible AI dialogue.

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Pickl AI

OCTOBER 28, 2024

This includes designing algorithms, building Machine Learning models, and integrating AI solutions into existing systems. The role requires a blend of skills in programming, data science, and engineering principles. They may also transition into specialised areas like natural language processing (NLP) or computer vision.

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Dataconomy

MARCH 23, 2023

Artificial intelligence is a branch of computer science that involves the development of intelligent machines that can perform tasks that typically require human intelligence. AI technologies include machine learning, natural language processing, computer vision, and robotics, among others.

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Pickl AI

OCTOBER 7, 2024

As we delve into the concept of “quantum leaps” in Machine Learning, it becomes essential to understand how these revolutionary concepts are reshaping industries and enhancing the efficiency of algorithms. This financial backing is accelerating breakthroughs in QML algorithms and applications.

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Dataconomy

FEBRUARY 24, 2023

Definition of artificial intelligence (AI) Artificial Intelligence (AI) is a field of computer science that focuses on developing algorithms and computer programs that can perform tasks that would typically require human intelligence to complete. AU seeks to augment human intelligence with AI tools and insights.

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Heartbeat

FEBRUARY 2, 2024

Visual Question Answering (VQA) stands at the intersection of computer vision and natural language processing, posing a unique and complex challenge for artificial intelligence. is a significant benchmark dataset in computer vision and natural language processing.

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Towards AI

MARCH 28, 2023

These technologies have revolutionized the way we interact with computers — enabling us to access information, make purchases, and carry out an array of tasks through simple voice commands or text messages. Conversational AI is concerned with the development of computer programs that can engage in human-like conversations with people.

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Pickl AI

AUGUST 1, 2024

Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms. Understanding Deep Learning Engineer A Deep Learning engineer is primarily responsible for creating and optimising algorithms that enable machines to learn from data.

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Pickl AI

JULY 10, 2024

AI encompasses various technologies and applications, from simple algorithms to complex neural networks. On the other hand, ML focuses specifically on developing algorithms that allow machines to learn and make predictions or decisions based on data. Key Features: Challenging problem sets to build coding and algorithm skills.

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Dataconomy

MARCH 8, 2023

AAI systems use complex algorithms and machine learning techniques to analyze data, learn from it, and make decisions based on the information obtained. This field is multidisciplinary, involving experts in computer science, robotics, electrical engineering, and other related fields.

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Pickl AI

NOVEMBER 5, 2024

What is Data Science and Artificial Intelligence? Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Bias in Algorithms Machine Learning models can inadvertently perpetuate biases present in training data.

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Data Science Dojo

JUNE 4, 2024

Some common topics discussed and covered in AI books include search algorithms, machine learning, natural language processing, and computer vision – the building blocks of intelligent systems. They cover multiple aspects of a topic in-depth, providing a detailed repository of information for readers.

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IBM Journey to AI blog

JULY 6, 2023

These computer science terms are often used interchangeably, but what differences make each a unique technology? To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. Computer vision is a factor in the development of self-driving cars.

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AWS Machine Learning Blog

SEPTEMBER 9, 2024

As LLMs have grown larger, their performance on a wide range of natural language processing tasks has also improved significantly, but the increased size of LLMs has led to significant computational and resource challenges. Training and deploying these models requires vast amounts of computing power, memory, and storage.

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