Learn to design real machine learning systems with the help of several open-ended machine learning problems.

Become a Machine Learning Engineer

Learn to design real machine learning systems with the help of several open-ended machine learning problems commonly asked during interviews at big tech companies. You will start by identifying the problem statement, understanding scale and latency requirements and defining metrics. Then you will come up with the architecture, select models, gather data and finally execute and evaluate the models offline and online.

Grokking the Machine Learning Interview

# Introduction
Named entity linking (NEL) is the process of detecting and linking entity mentions in a given text to corresponding entities in a target knowledge base.

There are two parts to entity linking:

1. *Named-entity recognition*
 
   Named-entity recognition (NER) detects and classifies potential [named entities](https://en.wikipedia.org/wiki/Named_entity) in the text into predefined categories such as a person, organization, location, medical code, time expression, etc. (multi-class prediction).

2. *Disambiguation*

   Next, disambiguation disambiguates each detected entity by linking it to its corresponding entity in the knowledge base.

> 📝 The target knowledge base depends on the application, but for generic systems, a common choice is Wikidata or DBpedia.

Let's see entity linking in action in the following example. 


Let's get acquainted with the task of building an entity linking system.

Before We Begin

Introduction

Practical ML Techniques/Concepts

Search Ranking

Feed Based System

Recommendation System

Self-Driving Car: Image Segmentation

Entity Linking System

Ad Prediction System

Conclusion

Problem Statement

Introduction