two examples of regression, classification, clistering and association that is used in the case study of linkedin LinkedIn is the largest professional social networking site, with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion monthly interactions. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the predictive analytics developed by data scientists at LinkedIn: i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems: This tool helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression. ii) Recommendation Systems Personalized for News Feed: The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, decision trees, and neural networks for recommendation systems. iii) CNN's to Detect Inappropriate Content: To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on its platform. Any form of spam, harassment, or inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a neural networks-based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user co LinkedIn is the largest professional social networking site, with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion monthly interactions. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inf.

1. two examples of regression, classification, clistering and association that is used in the
case study of linkedin
LinkedIn is the largest professional social networking site, with nearly 800 million
members in more than 200 countries worldwide. Almost 40% of the users access
LinkedIn daily, clocking around 1 billion monthly interactions. The data science team at
LinkedIn works with this massive pool of data to generate insights to build strategies,
apply algorithms and statistical inferences to optimize engineering solutions, and help
the company achieve its goals. Here are some of the predictive analytics developed by
data scientists at LinkedIn:
i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems: This
tool helps recruiters build and manage a talent pool to optimize the chances of hiring
candidates successfully. This sophisticated product works on search and
recommendation engines. The LinkedIn recruiter handles complex queries and filters on
a constantly growing large dataset. The results delivered have to be relevant and
specific. The initial search model was based on linear regression.
ii) Recommendation Systems Personalized for News Feed: The LinkedIn news feed is
the heart and soul of the professional community. A member's newsfeed is a place to
discover conversations among connections, career news, posts, suggestions, photos,
and videos. Every time a member visits LinkedIn, machine learning algorithms identify
the best exchanges to be displayed on the feed by sorting through posts and ranking
the most relevant results on top. The algorithms help LinkedIn understand member
preferences and help provide personalized news feeds. The algorithms used include
logistic regression, decision trees, and neural networks for recommendation systems.
iii) CNN's to Detect Inappropriate Content: To provide a professional space where
people can trust and express themselves professionally in a safe community has been a
critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect
fake accounts and abusive behavior on its platform. Any form of spam, harassment, or
inappropriate content is immediately flagged and taken down. These can range from
profanity to advertisements for illegal services. LinkedIn uses a neural networks-based
machine learning model. This classifier trains on a training dataset containing accounts
labeled as either "inappropriate" or "appropriate." The inappropriate list consists of
accounts having content from "blocklisted" phrases or words and a small portion of
manually reviewed accounts reported by the user co
LinkedIn is the largest professional social networking site, with nearly 800 million members in
more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking
around 1 billion monthly interactions. The data science team at LinkedIn works with this
massive pool of data to generate insights to build strategies, apply algorithms and statistical
inferences to optimize engineering solutions, and help the company achieve its goals. Here are
some of the predictive analytics developed by data scientists at LinkedIn:
i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems: This tool
helps recruiters build and manage a talent pool to optimize the chances of hiring candidates
successfully. This sophisticated product works on search and recommendation engines. The
LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The
results delivered have to be relevant and specific. The initial search model was based on linear
regression.

2. ii) Recommendation Systems Personalized for News Feed: The LinkedIn news feed is the heart
and soul of the professional community. A member's newsfeed is a place to discover
conversations among connections, career news, posts, suggestions, photos, and videos. Every
time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be
displayed on the feed by sorting through posts and ranking the most relevant results on top. The
algorithms help LinkedIn understand member preferences and help provide personalized news
feeds. The algorithms used include logistic regression, decision trees, and neural networks for
recommendation systems.
iii) CNN's to Detect Inappropriate Content: To provide a professional space where people can
trust and express themselves professionally in a safe community has been a critical goal at
LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and
abusive behavior on its platform. Any form of spam, harassment, or inappropriate content is
immediately flagged and taken down. These can range from profanity to advertisements for
illegal services. LinkedIn uses a neural networks-based machine learning model. This classifier
trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate."
The inappropriate list consists of accounts having content from "blocklisted" phrases or words
and a small portion of manually reviewed accounts reported by the user co
i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems: This tool
helps recruiters build and manage a talent pool to optimize the chances of hiring candidates
successfully. This sophisticated product works on search and recommendation engines. The
LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The
results delivered have to be relevant and specific. The initial search model was based on linear
regression.
ii) Recommendation Systems Personalized for News Feed: The LinkedIn news feed is the heart
and soul of the professional community. A member's newsfeed is a place to discover
conversations among connections, career news, posts, suggestions, photos, and videos. Every
time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be
displayed on the feed by sorting through posts and ranking the most relevant results on top. The
algorithms help LinkedIn understand member preferences and help provide personalized news
feeds. The algorithms used include logistic regression, decision trees, and neural networks for
recommendation systems.
iii) CNN's to Detect Inappropriate Content: To provide a professional space where people can
trust and express themselves professionally in a safe community has been a critical goal at
LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and
abusive behavior on its platform. Any form of spam, harassment, or inappropriate content is
immediately flagged and taken down. These can range from profanity to advertisements for
illegal services. LinkedIn uses a neural networks-based machine learning model. This classifier
trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate."
The inappropriate list consists of accounts having content from "blocklisted" phrases or words
and a small portion of manually reviewed accounts reported by the user co