IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

Mlearning.ai

NOVEMBER 29, 2023

All the previously, recently, and currently collected data is used as input for time series forecasting where future trends, seasonal changes, irregularities, and such are elaborated based on complex math-driven algorithms. This results in quite efficient sales data predictions.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

phData

AUGUST 24, 2023

With a modeled estimation of the applicant’s credit risk, lenders can make more informed decisions and reduce the occurrence of bad loans, thereby protecting their bottom line. Greater Accuracy Machine learning models can handle high-dimensional, nonlinear, and interactive relationships between variables.

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Key programming languages include Python and R, while mathematical concepts like linear algebra and calculus are crucial for model optimisation.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

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. Predictive Analytics One of the most remarkable aspects of Data Science in stock market analysis is its predictive capabilities.

Data Science 52

Data Science Machine Learning Machine Learning Algorithm 52

ML @ CMU

MAY 12, 2023

In short, this says that the (k)-th data silo may set its own ((varepsilon_k, delta_k)) example-level DP target for any learning algorithm with respect to its local dataset. Adaptability and explainability : The framework is highly adaptable to any learning algorithm that can take DP-SGD-style updates.

Data Silos 130

Data Silos Algorithm ML ML 130

AWS Machine Learning Blog

JUNE 22, 2023

Building a baseline model with AutoGluon With any ML use case, it’s important to establish a baseline model to be used for comparison and iteration. We used AutoGluon to explore several classic ML algorithms. AutoGluon is easy-to-use AutoML tool that uses automatic data processing, hyperparameter tuning, and model ensemble.

AWS 93

AWS ML ML Machine Learning 93

Pickl AI

JULY 23, 2023

Big Data Big data refers to vast volumes of information that exceed the processing capabilities of traditional databases. Characterized by the three Vs: volume, velocity, and variety, big data poses unique challenges and opportunities.

Analytics 52

Analytics Analytics Predictive Analytics Data Analysis 52

Cambridge Intelligence

FEBRUARY 20, 2024

FREE: Managing fraud The ultimate guide to fraud detection, investigation and prevention using data visualization GET YOUR FREE GUIDE The role of new & existing technology For many years, credit card companies have relied on analytics, algorithms and decision trees to power their fraud strategy.

AI 59

AI AI Machine Learning Machine Learning 59

DagsHub

OCTOBER 14, 2024

Machine learning workflows are a collection of multiple critical stages including data collection, preprocessing, model development, training, evaluation, and deployment. can significantly change the model performance. Sometimes including the hyperparameters information such as learning rate, batch size, accuracy, etc.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

DagsHub

APRIL 21, 2024

MLOps helps these organizations to continuously monitor the systems for accuracy and fairness, with automated processes for model retraining and deployment as new data becomes available. GPUs, TPUs) for fast and efficient model training and inference, making it suitable for large-scale ML tasks.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Dataconomy

SEPTEMBER 1, 2023

The time has come for us to treat ML and AI algorithms as more than simple trends. Hybrid machine learning techniques can help with effective heart disease prediction by combining the strengths of different machine learning algorithms and utilizing them in a way that maximizes their predictive power.

Decision Trees 195

Decision Trees Machine Learning Machine Learning Support Vector Machines 195