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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Pickl AI

JANUARY 3, 2024

Feature engineering in machine learning is a pivotal process that transforms raw data into a format comprehensible to algorithms. Through Exploratory Data Analysis , imputation, and outlier handling, robust models are crafted. Hence, it is important to discuss the impact of feature engineering in Machine Learning.

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Heartbeat

SEPTEMBER 25, 2023

Photo by National Cancer Institute on Unsplash Machine learning has recently been a game-changer in healthcare and medical diagnosis. However, the complexity and volume of medical data pose significant challenges to the human mind. This is where machine learning comes in.

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ODSC - Open Data Science

MARCH 13, 2023

Recently, we posted the first article recapping our recent machine learning survey. There, we talked about some of the results, such as what programming languages machine learning practitioners use, what frameworks they use, and what areas of the field they’re interested in. As the chart shows, two major themes emerged.

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ODSC - Open Data Science

NOVEMBER 1, 2023

Don’t lose hope just yet —  machine learning is stepping up as the transformative solution you’ve all been waiting for. How Machine Learning Works in Fraud Detection Data gathering is crucial. Next, machine learning models come into play. They can identify patterns and anomalies in the data.

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Mlearning.ai

FEBRUARY 21, 2023

This is where machine learning comes in — it has the potential to revolutionize payment security by providing more accurate and effective fraud detection and prevention mechanisms. As such, there is a growing need for more advanced and sophisticated methods of payment security, such as machine learning.

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Unite.AI

OCTOBER 12, 2023

research scientist with over 16 years of professional experience in the fields of speech/audio processing and machine learning in the context of Automatic Speech Recognition (ASR), with a particular focus and hands-on experience in recent years on deep learning techniques for streaming end-to-end speech recognition.

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Aiiot Talk

APRIL 30, 2024

Traditionally, NDT relied heavily on manual inspection techniques and human expertise, but the process has undergone a transformative evolution with the advent of AI and machine learning (ML). AI algorithms’ predictive analytic capabilities also help anticipate potential failures. How Is AI Used in NDT?

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Heartbeat

OCTOBER 30, 2023

Ensuring Long-Term Performance and Adaptability of Deployed Models Source: [link] Introduction When working on any machine learning problem, data scientists and machine learning engineers usually spend a lot of time on data gathering , efficient data preprocessing , and modeling to build the best model for the use case.

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Marktechpost

JULY 24, 2023

Revolutionary advances in machine learning (ML) algorithms have empowered many AI-powered applications in various industries, such as e-commerce, finance, manufacturing, and medicine. However, this approach often undervalues the importance of the data across the ML lifecycle.

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AI News

MAY 3, 2024

“Managing dynamic data quality, testing and detecting for bias and inaccuracies, ensuring high standards of data privacy, and ethical use of AI systems all require human oversight,” he said.

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IBM Journey to AI blog

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

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IBM Journey to AI blog

DECEMBER 20, 2023

This calls for the organization to also make important decisions regarding data, talent and technology: A well-crafted strategy will provide a clear plan for managing, analyzing and leveraging data for AI initiatives. Establish a data governance framework to manage data effectively.

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Unite.AI

OCTOBER 17, 2023

By adopting technologies like artificial intelligence (AI) and machine learning (ML), companies can give a boost to their customer segmentation efforts. In those cases, a traditional approach run by humans can work better, especially if you mainly have qualitative data. Here’s a guide to help you accomplish that.

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Marktechpost

AUGUST 24, 2023

As an alternative, offline RL algorithms are more computationally efficient and less vulnerable to reward hacking because they learn from a predefined dataset of samples. However, the characteristics of the offline dataset are inextricably linked to the quality of the policy learned offline. .

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Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

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Marktechpost

MAY 8, 2024

Extensions to the base DQN algorithm, like Double Q Learning and Prioritized replay, enhance its performance, offering promising avenues for autonomous driving applications. Existing standards like ISO 26262 provide a framework, but adapting them for deep learning is complex.

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Unite.AI

JUNE 6, 2023

I wasn’t surprised by these responses as they are commonly cited, and also of course because the data challenge is our organization’s reason for being. When it comes to data challenges, LXT can both source data and label it so that machine learning algorithms can make sense of it.

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Unite.AI

OCTOBER 4, 2023

For example, in August 2020, Robert McDaniel became the target of a criminal act due to the Chicago Police Department’s predictive policing algorithm labeling him as a “person of interest.” Similarly, the AI-generated image of a South Sudan Barbie was shown holding a gun at her side, reflecting the deeply rooted bias in AI algorithms.

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Pickl AI

DECEMBER 4, 2023

Imagine this: we collect loads of data, right? Data Intelligence takes that data, adds a touch of AI and Machine Learning magic, and turns it into insights. It’s not just about having data; it’s about turning that data into real wisdom for better products and services. These insights?

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ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out her talk, “ Power trusted AI/ML Outcomes with Data Integrity ,” there! Due to the tsunami of data available to organizations today, artificial intelligence (AI) and machine learning (ML) are increasingly important to businesses seeking competitive advantage through digital transformation.

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Unite.AI

SEPTEMBER 26, 2023

Data Quality and Availability AI models heavily depend on data to function effectively. If businesses don't provide clean, structured and comprehensive data, these models can produce inaccurate results, leading the system to make erroneous predictions.

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DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . No longer is the modeler only limited to using linear models; they may now make use of varied data sources (both structured and unstructured) to build significantly higher performing models to power business processes.

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Unite.AI

FEBRUARY 28, 2024

Automated LLM-based analysis of data sources using AI and machine learning -powered algorithms is not only the most effective way to extract these insights; in a world that gets more complicated and data-laden on a daily basis, it's really the only efficient option available.

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Towards AI

AUGUST 16, 2023

No Free Lunch Theorem: Any two algorithms are equivalent when their performance is averaged across all possible problems. The MLOps Process We can see some of the differences with MLOps which is a set of methods and techniques to deploy and maintain machine learning (ML) models in production reliably and efficiently.

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Flipboard

JULY 31, 2023

In this post, we’ll show you the datasets you can use to build your machine learning projects. After you create a free account, you’ll have access to the best machine learning datasets. Importance and Role of Datasets in Machine Learning Data is king.

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IBM Journey to AI blog

APRIL 25, 2024

Using advanced analytics and machine learning algorithms, such data streams can be transformed into deep insights that predict a target audience’s needs. To ensure the success of this approach, it is crucial to maintain a strong focus on data quality, security and ethical considerations.

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Unite.AI

FEBRUARY 27, 2024

Customer requests are increasingly going beyond the micro tasking machine learning inputs of the past toward LLMs, and the more complex data sets required by apps like ChatGPT, Gemini and the many offshoots. You have years of experience working in search and personalization to help improve these algorithms.

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Heartbeat

OCTOBER 25, 2023

Empowering Data Scientists and Machine Learning Engineers in Advancing Biological Research Image from European Bioinformatics Institute Introduction: In biological research, the fusion of biology, computer science, and statistics has given birth to an exciting field called bioinformatics.

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ODSC - Open Data Science

APRIL 25, 2023

Be sure to check out her talk, “ Hands-on Data-Centric AI: Data preparation tuning — why and how? Machine Learning is applied to an increasingly large number of applications that range from financial to healthcare industries. This step allows defining the limit of a Machine Learning performance given a base data quality.

Data Quality 52

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Pickl AI

OCTOBER 9, 2023

However, with the emergence of Machine Learning algorithms, the retail industry has seen a revolutionary shift in demand forecasting capabilities. This technology allows computers to learn from historical data, identify patterns, and make data-driven decisions without explicit programming.

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ODSC - Open Data Science

DECEMBER 5, 2023

We had bigger sessions on getting started with machine learning or SQL, up to advanced topics in NLP, and of course, plenty related to large language models and generative AI. Top Sessions With sessions both online and in-person in San Francisco, there was something for everyone.

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Viso.ai

JANUARY 30, 2024

During machine learning model training, there are seven common errors that engineers and data scientists typically run into. One of the most common problems in machine learning happens when the training data is not well fitted to what the machine learning model is supposed to learn.

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Pickl AI

OCTOBER 11, 2023

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data. What is Data Observability?

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Unite.AI

OCTOBER 2, 2023

When it comes to data quality, we realized a valid training set could not be generated from existing commercial or crowd-sourced data. The first thing we needed was a chemistry method that would provide visibility on the delicate balance of the volatile, nonvolatile, dissolved solids, spectral data, etc.,

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ODSC - Open Data Science

JANUARY 18, 2024

Cybersecurity Measures to Prevent Data Poisoning Bad actors always look for ways to twist AI algorithms into something more sinister, making data poisoning a serious issue that you should be prepared for. Weekly Recap Newsletter Want to get a weekly digest of AI news from around the world every Friday?

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Pickl AI

JULY 25, 2023

Proficient in programming languages like Python or R, data manipulation libraries like Pandas, and machine learning frameworks like TensorFlow and Scikit-learn, data scientists uncover patterns and trends through statistical analysis and data visualization. Big Data Technologies: Hadoop, Spark, etc.

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