Google Research AI blog

FEBRUARY 7, 2023

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. Below, we highlight a panoply of works that demonstrate Google Research’s efforts in developing new algorithms to address the above challenges.

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IBM Journey to AI blog

AUGUST 9, 2024

Predictive AI blends statistical analysis with machine learning algorithms to find data patterns and forecast future outcomes. It extracts insights from historical data to make accurate predictions about the most likely upcoming event, result or trend. What is predictive AI? Regression models determine correlations between variables.

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ODSC - Open Data Science

MAY 31, 2023

Apache Kafka is an event streaming platform that collects, stores, and processes streams of data (events) in real-time and in an elastic, scalable, and fault-tolerant manner. Consumers read the events and process the data in real-time. The TensorFlow instance acts as a Kafka consumer to load new events into its memory.

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Pickl AI

SEPTEMBER 7, 2023

When you start exploring more about Machine Learning, you will come across the Gradient Boosting Algorithm. Basically, it is a powerful and versatile machine-learning algorithm that falls under the category of ensemble learning. Machine Learning models can leave you spellbound by their efficiency and proficiency.

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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. Fundamental to any aspect of data science, it’s difficult to develop accurate predictions or craft a decision tree if you’re garnering insights from inadequate data sources.

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Mlearning.ai

FEBRUARY 10, 2024

Unlocking Predictive Power: How Bayes’ Theorem Fuels Naive Bayes Algorithm to Solve Real-World Problems [link] Introduction In the constantly shifting realm of machine learning, we can see that many intricate algorithms are rooted in the fundamental principles of statistics and probability. Take the Naive Bayes algorithm, for example.

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Dataconomy

OCTOBER 5, 2023

AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. Common choices include neural networks (used in deep learning), decision trees, support vector machines, and more. Over time, the algorithm improves its accuracy and can make better predictions on new, unseen data.

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Dataconomy

MAY 25, 2023

By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions. This enables them to extract valuable insights, identify patterns, and make informed decisions in real-time.

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Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Key Takeaways Entropy measures randomness, guiding algorithms to make better decisions.

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AWS Machine Learning Blog

JUNE 22, 2023

Over 500 machine events are monitored in near-real time to give a full picture of machine conditions and their operating environments. Light & Wonder teamed up with the Amazon ML Solutions Lab to use events data streamed from LnW Connect to enable machine learning (ML)-powered predictive maintenance for slot machines.

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Pickl AI

MAY 10, 2023

Machine Learning is a subset of Artificial Intelligence and Computer Science that makes use of data and algorithms to imitate human learning and improving accuracy. Being an important component of Data Science, the use of statistical methods are crucial in training algorithms in order to make classification. What is Classification?

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Pickl AI

APRIL 4, 2025

These tools enable data analysis, model building, and algorithm optimization, forming the backbone of ML applications. Feed data into an algorithm, and out comes predictions, classifications, or insights that seem almost intuitive. Think of ML algorithms as sophisticated tools. Event: A subset of the sample space.

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Ocean Protocol

NOVEMBER 28, 2024

Participants used historical data from past Mexican Grand Prix events and insights from the 2024 F1 season to create machine-learning models capable of predicting key race elements. With every second on the track critical, the challenge showcased how data can shape decisions that define race outcomes.

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Smart Data Collective

JUNE 26, 2019

Three years later, the code was released as hey solution for machine learning algorithms in conjunction with Google and several other major companies. Scikit-learn is a library that contains several implementations of machine learning algorithms. Decision tree pruning and induction. Decision boundary learning with SVMs.

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ODSC - Open Data Science

NOVEMBER 30, 2023

Explainable AI (XAI) refers to AI that explains how, where, and why it produces decisions. XAI coincides with white-box models, which detail the results the algorithms have. It uses data mining techniques like decision trees and rule-based systems to generate correct responses. Interested in attending an ODSC event?

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IBM Journey to AI blog

DECEMBER 19, 2023

As organizations collect larger data sets with potential insights into business activity, detecting anomalous data, or outliers in these data sets, is essential in discovering inefficiencies, rare events, the root cause of issues, or opportunities for operational improvements. But what is an anomaly and why is detecting it important?

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Pickl AI

AUGUST 6, 2023

Regression Machine Learning algorithms is a statistical method that you can use to model the relationship between dependent variables and one or more independent variables. Effectively, regression algorithms helps in determining the best-fit line. The algorithm is simple to interpret and can capture complex relationships in the data.

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Pickl AI

DECEMBER 12, 2024

Summary: XGBoost is a highly efficient and scalable Machine Learning algorithm. Introduction Boosting is a powerful Machine Learning ensemble technique that combines multiple weak learners, typically decision trees, to form a strong predictive model. Its flexibility and performance make it a cornerstone in predictive modelling.

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Dataconomy

OCTOBER 11, 2024

Predictive AI is designed to forecast future events based on historical data. This makes it more like a sophisticated crystal ball—but with data, algorithms, and statistical rigor behind it. Predictive AI is often used in scenarios that require informed decision-making. ” or “Which customers are most likely to churn?”

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ODSC - Open Data Science

NOVEMBER 13, 2023

Predictive analytics is rapidly becoming indispensable in data-driven decision-making, especially grant funding. It uses statistical algorithms and machine learning techniques to analyze historical data and predict future outcomes. Interested in attending an ODSC event? Learn more about our upcoming events here.

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ODSC - Open Data Science

SEPTEMBER 18, 2023

Classic Machine Learning in NLP The following section explores how traditional machine learning algorithms can be applied to NLP tasks. Interested in attending an ODSC event? Learn more about our upcoming events here. Subscribe to our weekly newsletter here and receive the latest news every Thursday.

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Pickl AI

SEPTEMBER 12, 2024

A Algorithm: A set of rules or instructions for solving a problem or performing a task, often used in data processing and analysis. Decision Trees: A supervised learning algorithm that creates a tree-like model of decisions and their possible consequences, used for both classification and regression tasks.

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Pickl AI

OCTOBER 3, 2024

Summary: Predictive analytics utilizes historical data, statistical algorithms, and Machine Learning techniques to forecast future outcomes. It also highlights diverse applications across industries such as healthcare, retail, finance, and marketing for enhanced decision-making. What is Predictive Analytics?

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AWS Machine Learning Blog

MAY 8, 2024

This meticulous approach allows Dialog Axiata to gain valuable insights into customer behavior, enabling them to predict potential churn events with remarkable accuracy. Concurrently, the ensemble model strategically combines the strengths of various algorithms. million subscribers, which amounts to 57% of the Sri Lankan mobile market.

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Mlearning.ai

APRIL 4, 2023

Instead of traditional segmentation based on hard business metrics, financial institutions can group customers into smaller micro-segments using fine-tuned ML algorithms. Conclusion In short, ML algorithms enable lenders to get meaningful insights on the expected losses in case of default, based on historical and current information.

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Heartbeat

OCTOBER 11, 2023

A key component of artificial intelligence is training algorithms to make predictions or judgments based on data. In both cases, algorithms are trained to generate predictions or judgments based on data inputs. Machine learning algorithms can make predictions or classifications based on input data. What is Machine Learning?

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Pickl AI

SEPTEMBER 3, 2023

Observations that deviate from the majority of the data are known as anomalies and might take the shape of occurrences, trends, or events that differ from customary or expected behaviour. Finding anomalous occurrences that might point to intriguing or potentially significant events is the aim of anomaly detection.

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Mlearning.ai

MAY 23, 2023

An interdisciplinary field that constitutes various scientific processes, algorithms, tools, and machine learning techniques working to help find common patterns and gather sensible insights from the given raw input data using statistical and mathematical analysis is called Data Science. Decision trees are more prone to overfitting.

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Pickl AI

AUGUST 23, 2023

By extracting insights from these datasets, professionals can make more informed investment decisions, reducing the risk associated with emotional biases. Through sophisticated algorithms and Machine Learning models , data scientists can predict stock price movements with a degree of accuracy that was previously unthinkable.

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ODSC - Open Data Science

JANUARY 5, 2024

Scikit-learn A machine learning powerhouse, Scikit-learn provides a vast collection of algorithms and tools, making it a go-to library for many data scientists. Interested in attending an ODSC event? Learn more about our upcoming events here. Subscribe to our weekly newsletter here and receive the latest news every Thursday.

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Dataconomy

FEBRUARY 24, 2023

Choose the appropriate algorithm: Select the AI algorithm that best suits the problem you want to solve. Several algorithms are available, including decision trees, neural networks, and support vector machines. This involves feeding the algorithm with data and tweaking it to improve its accuracy.

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IBM Journey to AI blog

FEBRUARY 23, 2024

ML algorithms understand language in the NLU subprocesses and generate human language within the NLG subprocesses. Predictive analytics integrates with NLP, ML and DL to enhance decision-making capabilities, extract insights, and use historical data to forecast future behavior, preferences and trends.

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Pickl AI

JULY 23, 2023

It requires sophisticated tools and algorithms to derive meaningful patterns and trends from the sheer magnitude of data. Real-time data feeds and algorithmic trading strategies have transformed the dynamics of financial markets. It involves investigating the root causes behind specific outcomes or trends observed in the data.

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Ocean Protocol

MARCH 11, 2024

Through this process, they gained valuable insights into Miami’s meteorological landscape, including seasonal variations and significant weather events impacting KMIA operations. Points will now be awarded to the top 10 finishers in each event, with 100 championship points up for grabs.

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Pickl AI

OCTOBER 15, 2024

They identify patterns in existing data and use them to predict unknown events. Techniques like linear regression, time series analysis, and decision trees are examples of predictive models. Popular clustering algorithms include k-means and hierarchical clustering.

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Heartbeat

JULY 18, 2023

NLP with RandomForest Random Forest is a widely used machine learning technique that employs an ensemble of decision trees to make predictions. This method involves creating multiple decision trees from a random selection of features and training each tree on a random sample of the data.

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ODSC - Open Data Science

OCTOBER 23, 2024

Many of these speakers are familiar faces at past ODSC events or are regular contributors to major AI and tech conferences. Matt Harrison’s Talk: Machine Learning with XGBoost XGBoost is one of the most widely used machine learning algorithms today, known for its speed and accuracy in decision tree-based models.

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