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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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If you’ve been following English Premier League football, you would know that Manchester United have not displayed top-tier football in nearly a decade, a club that was known for winning titles every year.
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Indian Summers (April to June) are extremely Hot and Humid. Surprisingly (or not so), 2022 is hotter, not just in the usual cities across the country, but also in places that normally are known for warm temperatures. Want to forecast or analyse for 2022 summer based on last decade’s data?
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Are you applying LR to your data? Know its assumptions before applying, here
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New to Hands-on ML? Take a look at the Hello world of ML and try it out yourselves here
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How do ML Competitions work? Take a look at the workflow and try it out yourselves here
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How does a basic Pytorch workflow look like? This notebook shows it.
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How do ML algorithms optimize the loss functions? This notebook implements Gradient descent from scratch in less 20 lines of code.
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How do Boosting ML algorithms work? This notebook shows a practical implementation of it.
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What are some important traits that people look for in their partner. Check them out here
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The Post can be viewed here
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Multilabel vs Multiclass Classification using OneVsRest Classifier in Sklearn
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Data Extraction from API is important with the increase in web data. This notebook shows how to extract data from Zomato’s API.
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The Challenges can be viewed here
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What is a topic model? what are the algorithms that power them? Read them here
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A simple Data Engineering pipeline using Google cloud platform
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Want to know how predictions are made by Machine learning algorithms like decision trees and Random Forest?
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This is list of concepts that entails Inferential Statistics.
When trying to make sense of the world using statistics, it’s important to consider which type of research design will provide you with the most accurate answer for the type of question you are asking.Typically, the selection of a particular design involves an amount of horse trading between the extent to which a research environment is able to be controlled versus only being able to be observed, and whether the research goal involves determining some underlying cause for a phenomenon (science), or whether the goal is to optimize the yield or output for a specific process while minimizing effort (technology).
Computer Vision
Descriptive statistics is the use of statistical and graphic techniques to present information about the data set being studied. Computing descriptive statistics and examining graphic displays of data is an advisable preliminary step in data analysis. Descriptive statistics and graphic displays are also the final product in some contexts: for instance, a business may want to monitor total volume of sales for its different locations without any desire to use that information to make inferences about other businesses.