Smart Data Collective

OCTOBER 13, 2021

As we have already said, the challenge for companies is to extract value from data, and to do so it is necessary to have the best visualization tools. Over time, it is true that artificial intelligence and deep learning models will be help process these massive amounts of data (in fact, this is already being done in some fields).

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Dataconomy

JANUARY 24, 2024

Their AI services encompass machine learning, predictive analytics, chatbots, and cognitive computing. Since its inception in 2009, KMS Technology has remained committed to delivering top-notch services in AI, data analytics, and software development.

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Dataconomy

MAY 25, 2023

AI algorithms can uncover hidden correlations within IoT data, enabling predictive analytics and proactive actions. Here are some ways AI enhances IoT devices: Advanced data analysis AI algorithms can process and analyze vast volumes of IoT-generated data.

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Smart Data Collective

MARCH 16, 2022

In other cases, advanced AI applications use a deep-learning approach to sift through big data to predict the prices of stocks in the near future. For instance, real-time car purchases can help predict the price of Rolls Royce shares in the near future. However, deep-learning approaches are comprehensive in theory.

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Data Science Dojo

AUGUST 16, 2024

Generative AI for Data Analytics – Understanding the Impact To understand the impact of generative AI for data analytics, it’s crucial to dive into the underlying mechanisms, that go beyond basic automation and touch on complex statistical modeling, deep learning, and interaction paradigms.

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Dataconomy

MARCH 27, 2025

For instance, according to Salesforce, 90% of hospitals are expected to adopt AI agents by 2025, using predictive analytics and automation to improve patient outcomes. Natural Language Processing analyses customer sentiment, while biometrics and predictive personalisation enhance security and provide tailored recommendations.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Being able to discover connections between variables and to make quick insights will allow any practitioner to make the most out of the data. Analytics and Data Analysis Coming in as the 4th most sought-after skill is data analytics, as many data scientists will be expected to do some analysis in their careers.

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Towards AI

JUNE 27, 2023

Offering features like TensorBoard for data visualization and TensorFlow Extended (TFX) for implementing production-ready ML pipelines, TensorFlow stands out as a comprehensive solution for both beginners and seasoned professionals in the realm of machine learning.

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Data Science Blog

OCTOBER 20, 2024

Additionally, it allows for quick implementation without the need for complex calculations or data analysis, making it a convenient choice for organizations looking for a simple attribution method. One of its main advantages is its simplicity; it is a straightforward and easy-to-understand approach.

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Pickl AI

DECEMBER 3, 2024

According to a report by McKinsey, companies that harness AI in their analytics processes can achieve productivity gains of up to 40% and improve decision-making speed by 5-10 times. As businesses increasingly rely on data-driven strategies, the integration of GenAI tools has become essential for enhancing Data Analysis capabilities.

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ODSC - Open Data Science

MARCH 11, 2024

NLP and LLMs The NLP and LLMs track will give you the opportunity to learn firsthand from core practitioners and contributors about the latest trends in data science languages and tools, such as pre-trained models, with use cases focusing on deep learning, speech-to-text, and semantic search.

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Dataconomy

FEBRUARY 6, 2024

These companies are using AI and ML to improve existing processes, reduce risks, and predict business performance and industry trends. When it comes to the role of AI in information technology, machine learning, with its deep learning capabilities, is the best use case.

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ODSC - Open Data Science

AUGUST 21, 2023

NLP and LLMs The NLP and LLMs track will give you the opportunity to learn firsthand from core practitioners and contributors about the latest trends in data science languages and tools, such as pre-trained models, with use cases focusing on deep learning, speech-to-text, and semantic search.

Data Science 52

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Defined.ai blog

AUGUST 10, 2023

Businesses must understand how to implement AI in their analysis to reap the full benefits of this technology. In the following sections, we will explore how AI shapes the world of financial data analysis and address potential challenges and solutions.

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IBM Journey to AI blog

DECEMBER 20, 2023

For instance, if data scientists were building a model for tornado forecasting, the input variables might include date, location, temperature, wind flow patterns and more, and the output would be the actual tornado activity recorded for those days. temperature, salary).

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Pickl AI

SEPTEMBER 3, 2024

ReLU is widely used in Deep Learning due to its simplicity and effectiveness in mitigating the vanishing gradient problem. Tanh (Hyperbolic Tangent): This function maps input values to a range between -1 and 1, providing a smooth gradient for learning. This process typically involves backpropagation and optimisation techniques.

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Pickl AI

SEPTEMBER 23, 2024

Summary: The blog explores the synergy between Artificial Intelligence (AI) and Data Science, highlighting their complementary roles in Data Analysis and intelligent decision-making. Introduction Artificial Intelligence (AI) and Data Science are revolutionising how we analyse data, make decisions, and solve complex problems.

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Pickl AI

NOVEMBER 2, 2023

A cheat sheet for Data Scientists is a concise reference guide, summarizing key concepts, formulas, and best practices in Data Analysis, statistics, and Machine Learning. Here, we’ll explore why Data Science is indispensable in today’s world. Is Data Scientist math heavy?

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Mlearning.ai

APRIL 10, 2023

With the emergence of data science and AI, clustering has allowed us to view data sets that are not easily detectable by the human eye. Thus, this type of task is very important for exploratory data analysis. 3 feature visual representation of a K-means Algorithm.

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IBM Journey to AI blog

JULY 6, 2023

Machine learning can then “learn” from the data to create insights that improve performance or inform predictions. Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis.

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ODSC - Open Data Science

AUGUST 9, 2023

In retail and e-commerce, AI is used for demand forecasting and customer behavior analysis to enhance operational efficiency and customer satisfaction, as demonstrated by Amazon and Sephora. As seen in Google Ads, advertising uses AI for data analysis to create targeted and personalized ads, increasing consumer engagement and ROI.

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IBM Journey to AI blog

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

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IBM Journey to AI blog

SEPTEMBER 6, 2023

From customized content creation to task automation and data analysis, AI has seemingly endless applications when it comes to marketing, but also some potential risks. More meaningful insights from customer data: Today, many marketers struggle with the sheer amount of data available to them when they’re planning a campaign.

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Pickl AI

DECEMBER 4, 2023

In the realm of Data Intelligence, the blog demystifies its significance, components, and distinctions from Data Information, Artificial Intelligence, and Data Analysis. Key Components of Data Intelligence In Data Intelligence, understanding its core components is like deciphering the secret language of information.

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Pickl AI

APRIL 2, 2024

There are three main types, each serving a distinct purpose: Descriptive Analytics (Business Intelligence): This focuses on understanding what happened. Think of it as summarizing past data to answer questions like “Which products are selling best?” Unsupervised Learning: Finding patterns or insights from unlabeled data.

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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. Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models.

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Heartbeat

OCTOBER 23, 2023

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

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Pickl AI

DECEMBER 16, 2024

Summary: AI in Time Series Forecasting revolutionizes predictive analytics by leveraging advanced algorithms to identify patterns and trends in temporal data. This is due to the growing adoption of AI technologies for predictive analytics. Making Data Stationary: Many forecasting models assume stationarity.

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Pickl AI

MAY 29, 2024

Understanding Data Science Data Science is a multidisciplinary field that combines statistics, mathematics, computer science, and domain-specific knowledge to extract insights and wisdom from structured and unstructured data. Data Science has been critical in providing insights and solutions based on Data Analysis.

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Pickl AI

APRIL 17, 2023

The creation of AI has been possible by creating algorithms and computer programs that, without being specifically built for each task, may learn from experience and data input. Accordingly, the accomplishment of these tasks is possible through machine learning, deep learning, and techniques for processing natural language.

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Pickl AI

JANUARY 13, 2025

Machine Learning for Dummies By John Paul Mueller and Luca Massaron This book introduces the basics of Machine Learning with practical examples. Using simple language, it explains how to perform data analysis and pattern recognition with Python and R. Key Features: Easy-to-follow introduction to Machine Learning.

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Pickl AI

SEPTEMBER 11, 2024

Employers often look for candidates with a deep understanding of Data Science principles and hands-on experience with advanced tools and techniques. With a master’s degree, you are committed to mastering Data Analysis, Machine Learning, and Big Data complexities.

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Pickl AI

NOVEMBER 18, 2024

Underfitting happens when a model is too simplistic and fails to capture the underlying patterns in the data, leading to poor predictions. Common Applications of Machine Learning Machine Learning has numerous applications across industries. How Do I Choose the Right Machine Learning Model?

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Pickl AI

SEPTEMBER 26, 2024

Deep Knowledge of AI and Machine Learning : A solid understanding of AI principles, Machine Learning algorithms, and their applications is fundamental. Data Science Proficiency : Skills in Data Analysis, statistics, and the ability to work with large datasets are critical for developing AI-driven insights and solutions.

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Pickl AI

JULY 20, 2023

It involves deeper analysis and investigation to identify the root causes of problems or successes. Root cause analysis is a typical diagnostic analytics task. 3. Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes.

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Heartbeat

NOVEMBER 20, 2023

Blockchain network security can be further strengthened with machine learning algorithms, which can analyze data patterns and detect anomalies. Machine learning can identify potential cyber threats, fraudulent activities, and abnormal behaviors, helping prevent attacks and unauthorized access.

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Pickl AI

JULY 31, 2023

Deep Learning A subset of machine learning, Deep Learning involves neural networks with multiple layers that can automatically learn representations of data, leading to more complex and abstract reasoning. Autonomous vehicles and drones are examples of AI systems with decision-making capabilities.

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