Mlearning.ai

JANUARY 27, 2023

We use some of the data for training and some for testing (we will not use test data for training). How we do this is the subject of the concept of cross-validation. I will develop a model using the training data (blue) and apply it to my test data (red). Diagram of k-fold cross-validation.

Cross Validation 52

Cross Validation Machine Learning Machine Learning Data Mining 52

Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms. We pay our contributors, and we don't sell ads.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Mlearning.ai

APRIL 10, 2023

No Problem: Using DBSCAN for Outlier Detection and Data Cleaning Photo by Mel Poole on Unsplash DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. DBSCAN works by partitioning the data into dense regions of points that are separated by less dense areas.

Algorithm 52

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Pickl AI

MAY 24, 2023

Scikit-Learn Scikit Learn is associated with NumPy and SciPy and is one of the best libraries helpful for working with complex data. Its modified feature includes the cross-validation that allowing it to use more than one metric. It is clear that implementation of this library for ML dimension.

Data Science 52

Data Science Python Machine Learning Machine Learning 52

DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD. What is MLOps?

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Mlearning.ai

MAY 23, 2023

Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data.

Data Science 52

Data Science Decision Trees Deep Learning Deep Learning 52

Dataconomy

SEPTEMBER 1, 2023

The time has come for us to treat ML and AI algorithms as more than simple trends. Several data mining and neural network techniques have been employed to gauge the severity of heart disease but the prediction of it is a different subject. Ensuring that hybrid models also generalize well to unseen data is a constant concern.

Decision Trees 195

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