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The 2021 Executive Guide To Data Science and AI

Applied Data Science

Download the free, unabridged version here. They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. This allows for a much richer interpretation of predictions, without sacrificing the algorithm’s power.

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How To Learn Python For Data Science?

Pickl AI

Mathematics is critical in Data Analysis and algorithm development, allowing you to derive meaningful insights from data. Statistics Understand descriptive statistics (mean, median, mode) and inferential statistics (hypothesis testing, confidence intervals). These concepts help you analyse and interpret data effectively.

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Benford’s Law Meets Machine Learning for Detecting Fake Twitter Followers

Towards AI

Traditional methods for detecting fake accounts often rely on complex machine-learning algorithms. In this blog, I delve into the fascinating intersection of Benford’s Law and machine learning, exploring how this mathematical principle can be employed alongside advanced algorithms to expose and combat the presence of fake Twitter followers.

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Exploratory v6.2 Released!

learn data science

Unlike the Prophet, which is another Time Series Forecasting algorithm, the ARIMA requires more knowledge around how it builds the model. Hypothesis Test — t Test The t Test is to see if the difference between the means of two groups is significant or not. And, download Exploratory v6.2 That’s all!

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How to Build a Data Analyst Portfolio?

Pickl AI

Methodology: Explain the techniques, algorithms, or statistical methods you applied during the analysis. Resume Download (PDF): Provide a downloadable PDF version of your resume so that interested parties can keep it for future reference. Results: Present the insights and conclusions you derived from the analysis.

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Automating Model Risk Compliance: Model Validation

DataRobot Blog

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

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