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The Science of T20 Cricket: Decoding Player Performance with Predictive Modeling

Analytics Vidhya

With franchise leagues like IPL and BBL, teams rely on statistical models and tools for competitive edge. Python programming predicts player performances, aiding team selections and game tactics. Python programming predicts player performances, aiding team selections and game tactics.

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Statistics 101: Introduction to the Central Limit Theorem (with implementation in R)

Analytics Vidhya

Introduction What is one of the most important and core concepts of statistics that enables us to do predictive modeling, and yet it often. The post Statistics 101: Introduction to the Central Limit Theorem (with implementation in R) appeared first on Analytics Vidhya.

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What is the Difference Between Covariance and Correlation?

Analytics Vidhya

Introduction Comprehending and unleashing the intricate affinities among variables in the expansive realm of statistics is integral. Everything from data-driven decision-making to scientific discoveries to predictive modeling depends on our potential to disentangle the hidden connections and patterns within complex datasets.

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How to Use Data Science for Marketing?

Analytics Vidhya

Data science for marketing is a discipline that combines statistical analysis, machine learning, and predictive modeling to extract meaningful patterns […] The post How to Use Data Science for Marketing? appeared first on Analytics Vidhya.

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Machine Learning Paradigms with Example

Analytics Vidhya

Machine Learning is the method of teaching computer programs to do a specific task accurately (essentially a prediction) by training a predictive model using various statistical algorithms leveraging data. Introduction Let’s have a simple overview of what Machine Learning is. Source: [link] For […].

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Top 5 Statistical Techniques in Python

Sisense

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistics and programming go hand in hand. Mastering statistical techniques and knowing how to implement them via a programming language are essential building blocks for advanced analytics. Actual Predicted 23.1

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Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

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