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

## Who is this module for?
This module is designed for anyone who is aiming to design real machine learning systems and appear for a job interview for such a position. 

## Module structure
This module contains *seven* main chapters, namely:

1. __Practical ML Techniques/Concepts__ teaches us the practical techniques used in _machine learning_ that are needed by a professional machine learning engineer.
2. __Search Ranking__ covers the _search ranking_ problem and provides a solution overview to help us prepare better for a job interview.
3. __Feed Based System__ covers the _feed-based_ system problem and provides a solution overview to help us prepare better for a job interview.
4. __Recommendation System__ covers the _recommendation system_ problem and provides a solution overview to help us prepare better for a job interview.
5. __Self-Driving Car: Image Segmentation__ covers the _self-driving cars_ problem and provides a solution overview to help us prepare better for a job interview.
6. __Entity Linking System__ covers the _entity linking system_ problem and provides a solution overview to help us prepare better for a job interview.
7. __Ad Prediction System__ covers the _ad prediction_ system problem and provides a solution overview to help us prepare better for a job interview.

# Who is this module for?
This module is designed for anyone who is aiming to design real machine learning systems and appear for a job interview for such a position. 

# Module structure
This module contains *seven* main chapters, namely:

1. __Practical ML Techniques/Concepts__ teaches us the practical techniques used in _machine learning_ that are needed by a professional machine learning engineer.
2. __Search Ranking__ covers the _search ranking_ problem and provides a solution overview to help us prepare better for a job interview.
3. __Feed Based System__ covers the _feed-based_ system problem and provides a solution overview to help us prepare better for a job interview.
4. __Recommendation System__ covers the _recommendation system_ problem and provides a solution overview to help us prepare better for a job interview.
5. __Self-Driving Car: Image Segmentation__ covers the _self-driving cars_ problem and provides a solution overview to help us prepare better for a job interview.
6. __Entity Linking System__ covers the _entity linking system_ problem and provides a solution overview to help us prepare better for a job interview.
7. __Ad Prediction System__ covers the _ad prediction_ system problem and provides a solution overview to help us prepare better for a job interview.

This lesson discusses the intended audience and the necessary prerequisites of the module.

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

About this Module

Who is this module for?

Module structure