Machine Learning System Design is an important component of any ML interview. The ability to address problems, identify requirements, and discuss tradeoffs helps you stand out among hundreds of other candidates. Readers of this course able to get offers from Snapchat, Facebook, Coupang, Stitchfix and LinkedIn.

This course will help you understand the state of the practice on model techniques along with best practices in applying ML models in production at scale. Once you finished the course you can learn more use-cases at: http://mlengineer.io/

Once you’re done with the course, you will be able to apply and leverage knowledge from top researchers at tech companies. You will have up to date knowledge in model techniques from hundreds of the latest research and industry papers. There is even a chance that the interviewers will be surprised at the depth of your knowledge.

What You’ll Learn

• Improve your Machine Learning System Design skills. Apply the best techniques in order to structure and drive your interview.

Course Content

1.Machine Learning Primer

Get familiar with core machine learning principles, from feature engineering to model deployment.

• Introduction
• Feature Selection and Feature Engineering
• Training Pipeline
• Inference
• Metrics Evaluation

2.Video Recommendation

Discover the logic behind developing and optimizing scalable video recommendation systems for enhanced user engagement.

• Problem Statement and Metrics
• Candidate Generation and Ranking Model
• Video Recommendation System Design

3.Feed Ranking

Work your way through optimizing feed ranking with personalized models for enhanced user engagement.

• Problem Statement and Metrics
• Feed Ranking Model
• Feed Ranking System Design

4.Ad Click Prediction

Enhance your skills in designing and optimizing ad click prediction models for better ad performance.

• Problem Statement and Metrics
• Ad Click Prediction Model
• Ads Recommendation System Design

5.Rental Search Ranking

Take a closer look at designing Airbnb’s rental search ranking system with a booking prediction model and performance metrics.

• Problem Statement and Metrics
• Booking Model
• Rental Search Ranking System Design

6.Estimate Food Delivery Time

See how it works to design an accurate, scalable food delivery time estimation system.

• Problem Statement and Metrics
• Estimated Delivery Model
• Estimate Food Delivery System Design

7.Conclusion

Build on comprehensive insights into designing practical Machine Learning systems for diverse applications.

• Summary