System design is an important component of any ML interview. Being able to efficiently solve open-ended machine learning problems is a key skill that can set you apart from other engineers and increase the level of seniority at which you’re hired.

This course helps you build that skill, and goes over some of the most popularly asked interview problems at big tech companies. You’ll walk step-by-step through solving these problems, focusing in particular on how to design machine learning systems rather than just answering trivia-style questions.

Once you’re done with the course, you’ll be able to not just ace the machine learning interview at any tech company, but impress them with your ability to think about systems at a high level. If you have a machine learning or system design interview coming up, you’ll find the course tremendously valuable.

Course Content

1.Introduction

Get familiar with the essentials of ML interviews and key steps in designing ML systems.

• How Does This Course Help in ML Interviews?
• Setting up a Machine Learning System

2.Practical ML Techniques/Concepts

Walk through practical ML strategies, covering performance, data collection, experimentation, embeddings, transfer learning, and model debugging.

• Performance and Capacity Considerations
• Training Data Collection Strategies
• Online Experimentation
• Embeddings
• Transfer Learning
• Model Debugging and Testing

3.Search Ranking

Work your way through designing search ranking systems, selecting metrics, and filtering results effectively.

• Problem Statement
• Metrics
• Architectural Components
• Document Selection
• Feature Engineering
• Training Data Generation
• Ranking
• Filtering Results

4.Feed Based System

Build a foundation in designing and optimizing a Twitter feed system for user engagement.

• Problem Statement
• Metrics
• Architectural Components
• Tweet Selection
• Feature Engineering
• Training Data Generation
• Ranking
• Diversity
• Online Experimentation

5.Recommendation System

Generate personalized recommendations by leveraging data on user interactions, watch history, and preferences.

• Problem Statement
• Metrics
• Architectural Components
• Feature Engineering
• Candidate Generation
• Training Data Generation
• Ranking

6.Self-Driving Car: Image Segmentation

See how it works to enhance self-driving cars with advanced image segmentation techniques.

• Problem Statement
• Metrics
• Architectural Components
• Training Data Generation
• Modeling

7.Entity Linking System

Build on named entity linking (NEL) with recognition, disambiguation, metrics, architecture, and modeling insights.

• Problem Statement
• Metrics
• Architectural Components
• Training Data Generation
• Modeling

8.Ad Prediction System

Learn how to use machine learning to optimize ad relevance and user engagement.

• Problem Statement
• Metrics
• Architectural Components
• Feature Engineering
• Training Data Generation
• Ad Selection
• Ad Prediction