Cross Validation, Data Modeling and Hypothesis Testing

Must-Have Skills for a Machine Learning Engineer

Pickl AI

NOVEMBER 28, 2024

Concepts such as probability distributions, hypothesis testing , and Bayesian inference enable ML engineers to interpret results, quantify uncertainty, and improve model predictions. Model evaluation and tuning involve several techniques to assess and optimise model accuracy and reliability.

Machine Learning

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Types of Statistical Models in R for Data Scientists

Pickl AI

AUGUST 29, 2023

Parameter Estimation: Determine the parameters if the model by finding relevance to the data. This may involve finding values that best represent to observed data. Model Evaluation: Assess the quality of the midel by using different evaluation metrics, cross validation and techniques that prevent overfitting.

Data Scientist

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Must-Have Skills for a Machine Learning Engineer

Types of Statistical Models in R for Data Scientists

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