Data Science Dojo

AUGUST 24, 2023

MLOps facilitates automated testing mechanisms for ML models, which detects problems related to model accuracy, model drift, and data quality. Data collection and preprocessing The first stage of the ML lifecycle involves the collection and preprocessing of data.

Machine Learning 195

Machine Learning Machine Learning ML ML 195

Heartbeat

OCTOBER 23, 2023

Algorithm Development and Validation: Data scientists and machine learning engineers are responsible for developing and validating algorithms that power health informatics applications. However, ensuring data quality can be a significant challenge.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

Pickl AI

AUGUST 9, 2024

Data Cleaning and Transformation Techniques for preprocessing data to ensure quality and consistency, including handling missing values, outliers, and data type conversions. Students should learn about data wrangling and the importance of data quality.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

APRIL 26, 2024

Overfitting occurs when a model learns the training data too well, including noise and irrelevant patterns, leading to poor performance on unseen data. Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. In my previous role, we had a project with a tight deadline.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Heartbeat

MAY 29, 2023

Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data. Some of the steps that can be taken include: Data Governance: Implementing rigorous data governance policies that ensure fairness, transparency, and accountability in the data used to train LLMs.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78