Smart Data Collective

DECEMBER 19, 2021

Accordingly, before using that data in machine learning or an algorithm, you need to convert it into a precise format suitable for the system to inherit it. For instance, the Random Forest Algorithm in Python doesn’t support null values. Hence, data preprocessing is essential and required.

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Python Machine Learning Machine Learning Algorithm 141

Dataconomy

AUGUST 25, 2023

Performance metrics in machine learning are the most accurate way to measure how close your algorithm is to what you want. As we develop and fine-tune machine learning models, it’s imperative to have a way to measure their performance accurately. Just as students learn from textbooks, the algorithm learns from data examples.

Algorithm 91

Algorithm ML ML Machine Learning 91

Hacker News

FEBRUARY 26, 2024

Thus, power and time are saved through parallel execution and usage of processing components with local memory elements, optimized for running data-intensive algorithms. Specifically, MeMPA can perform up to three different instructions, each on different data blocks, concurrently.

Algorithm 130

Algorithm Data Modeling Data Models 130

AWS Machine Learning Blog

NOVEMBER 15, 2023

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model.

Algorithm 105

Algorithm AWS ML ML 105

Smart Data Collective

JUNE 4, 2021

Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models.

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Data Science Exploratory Data Analysis Machine Learning Machine Learning 133

Dataconomy

MAY 2, 2023

They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. In the context of a business, machine learning engineers are responsible for creating bots that are utilized for chat purposes or data collection. They build the future.

ML 110

ML ML Machine Learning Machine Learning 110

phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

Data Science Dojo

JANUARY 16, 2023

Every individual analysis the data obtained via their experience to generate a final decision. Put more concretely, data analysis involves sifting through data, modeling it, and transforming it to yield information that guides strategic decision-making.

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Data Analysis Data Analysis Data Analyst Big Data 195

Data Science Dojo

JANUARY 10, 2023

So, what makes a good data science profile? On the technical end, there are mathematical concepts, algorithms, data structures, etc. To conclude, a tool can be an excellent way to implement data science skills. Data scientists only work on predictive modeling Another myth!

Data Science 195

Data Science Data Scientist Data Analyst Machine Learning 195

Smart Data Collective

AUGUST 31, 2020

Predictive analytics models have proven to be remarkably effective with the stock futures market. One company that uses big data to forecast stock prices has found that its algorithms outperform similar forecasts by 26%. Big data is changing the tide with stock futures trading. How do these algorithms work so effectively?

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Big Data Big Data Algorithm Machine Learning 104

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Data Science Dojo

FEBRUARY 6, 2025

Large Language Models ( LLMs ) have emerged as a cornerstone technology in the rapidly evolving landscape of artificial intelligence. These models are trained using vast datasets and powered by sophisticated algorithms. Below are a few reasons that make data annotation a critical component for language models.

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AI AI ML ML 200

Dataversity

JANUARY 13, 2021

In marketing, artificial intelligence (AI) is the process of using data models, mathematics, and algorithms to generate insights that marketers can use. Click here to learn more about Gilad David Maayan. What Is Artificial Intelligence Marketing?

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Artificial Intelligence Artificial Intelligence Data Modeling Data Models 69

The Data Administration Newsletter

AUGUST 17, 2021

If you are planning on using predictive algorithms, such as machine learning or data mining, in your business, then you should be aware that the amount of data collected can grow exponentially over time.

Data Mining 115

Data Mining Data Mining Data Mining Big Data 115

Smart Data Collective

OCTOBER 20, 2021

AI-data mapping tools allow even non-technical business users to create intelligent data mappings using Machine Learning algorithms. Not only will this increase the speed but also the accuracy of the data mapping process. Legacy solutions lack precision and speed while handling big data.

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ML ML Big Data Big Data 133

Data Science Dojo

MARCH 7, 2023

These skills include programming languages such as Python and R, statistics and probability, machine learning, data visualization, and data modeling. To perform exploratory data analysis effectively, data scientists must have a strong understanding of math and statistics.

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Data Scientist Exploratory Data Analysis Data Science Data Visualization 221

Heartbeat

MARCH 8, 2023

Data mining, text classification, and information retrieval are just a few applications. To extract themes from a corpus of text data and then use these themes as features in text classification algorithms, topic modeling can be used in text classification. Naive Bayes is commonly used for spam classification.

Machine Learning 52

Machine Learning Machine Learning Algorithm Data Mining 52

Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

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Analytics Analytics Big Data Analytics Big Data Analytics 203

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

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Predictive Analytics Analytics Analytics Decision Trees 139

ODSC - Open Data Science

APRIL 28, 2023

Whether it’s an insurance company leveraging location for better underwriting or risk assessment, a financial services organization enriching transactions for validation and accurate merchant assignment, or a telecommunications company optimizing 5G rollouts and creating new services, there’s one essential commonality: location data.

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ML ML Data Silos Data Quality 98

Smart Data Collective

MAY 29, 2023

This bias can emerge due to multiple factors, such as the training data, algorithmic design, and human influence. Recognizing and comprehending the different forms of algorithm bias is crucial to develop effective strategies for bias mitigation.

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AI AI Algorithm Artificial Intelligence 96

AWS Machine Learning Blog

SEPTEMBER 26, 2024

During the iterative research and development phase, data scientists and researchers need to run multiple experiments with different versions of algorithms and scale to larger models. However, they require more sophisticated modeling techniques and increased computational resources.

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Clustering Algorithm ML ML 112

IBM Journey to AI blog

APRIL 5, 2024

Predict functionality builds predictive models to predict imminent failures and calculate assets’ remaining useful life. These models often incorporate machine learning and AI algorithms to detect the onset of degradation mechanisms in an early stage.

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Data Modeling Data Models Machine Learning Machine Learning 82

Data Science Connect

JULY 25, 2023

Data scientists must strike a balance between the platform’s simplicity and the customization required for complex data models and algorithms. Additionally, the use of these platforms may raise security concerns, as sensitive data could be mishandled by non-experts.

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Data Scientist Data Analysis Data Analysis Data Models 100

Smart Data Collective

NOVEMBER 24, 2019

One of the biggest applications is that new predictive analytics models are able to get a better understanding of the relationships between employees and find areas where they break down. These big data algorithms can offer insights to improve harmony within the team. Big Data is the Key to Stronger Team Extension Models.

Big Data 101

Big Data Big Data Predictive Analytics Data Models 101

Pickl AI

AUGUST 1, 2023

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. What is Machine Learning?

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Machine Learning Machine Learning Support Vector Machines Decision Trees 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists.

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Data Science Analytics Analytics Data Scientist 94

Smart Data Collective

NOVEMBER 18, 2020

Data mining is an automated data search based on the analysis of huge amounts of information. Complex mathematical algorithms are used to segment data and estimate the likelihood of subsequent events. Every Data Scientist needs to know Data Mining as well, but about this moment we will talk a bit later.

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Data Mining Data Mining Data Mining Data Science 144

ODSC - Open Data Science

OCTOBER 7, 2024

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Training: Running computations to learn from the data.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Cambridge Intelligence

OCTOBER 25, 2023

When you design your data model, you’ll probably begin by sketching out your data in a graph format – representing entities as nodes and relationships as links. Working in a graph database means you can take that whiteboard model and apply it directly to your schema with relatively few adaptations.

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Database Data Modeling Data Models Algorithm 64

Flipboard

JULY 31, 2023

Importance and Role of Datasets in Machine Learning Data is king. Algorithms are important and require expert knowledge to develop and refine, but they would be useless without data. These datasets, essentially large collections of related information, act as the training field for machine learning algorithms.

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Machine Learning Machine Learning Deep Learning Deep Learning 123

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. What is Supervised Learning?

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Supervised Learning Machine Learning Machine Learning Clustering 52

Pickl AI

JULY 31, 2023

Examples of Eager Learning Algorithms: Logistic Regression : A classic Eager Learning algorithm used for binary classification tasks. Support Vector Machines (SVM) : SVM is a powerful Eager Learning algorithm used for both classification and regression tasks. Eager Learning Algorithms: How does it work?

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

AWS Machine Learning Blog

DECEMBER 9, 2024

The scenario is using the XGBoost algorithm to train a binary classification model. Both the data processing job and model training job use @remote decorator so that the jobs are running in the SageMaker-associated private subnets and security group from your private VPC. config_yaml = f""" SchemaVersion: '1.0'

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AWS ML ML Data Scientist 85

Mlearning.ai

APRIL 12, 2023

ML algorithms, on the other hand, can analyze large amounts of performance data quickly and accurately, providing developers with insights into performance bottlenecks and areas for optimization. Personalized optimization strategies: ML algorithms can analyze user behavior data to create personalized optimization strategies.

Machine Learning 52

Machine Learning Machine Learning Decision Trees ML 52

Tableau

DECEMBER 19, 2022

Tableau’s search algorithm has been tuned to incorporate recency, frequency, popularity, and other signals about activity in Tableau. We’ve made it easier to: Manage tables in your data model. Swap any table with the root table in a single click for more flexibility with data models. Dynamically load images.

Tableau 98

Tableau Data Models Data Modeling Analytics 98

Tableau

DECEMBER 19, 2022

Tableau’s search algorithm has been tuned to incorporate recency, frequency, popularity, and other signals about activity in Tableau. We’ve made it easier to: Manage tables in your data model. Swap any table with the root table in a single click for more flexibility with data models. Dynamically load images.

Tableau 97

Tableau Data Models Data Modeling Analytics 97