HomeCoursesGrokking the Machine Learning Interview

Intermediate

15h

Grokking the Machine Learning Interview

Your proven path to success in Machine Learning Interviews – developed by FAANG engineers. Unlock ML loops at top companies with a System Design approach.

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Overview

Content

Reviews

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.

System design is an important component of any ML interview. Being able to efficiently solve open-ended machine learning problem...Show More

TAKEAWAY SKILLS

Machine Learning

Prepare for Interview

Content

49 Lessons

Introduction

2 Lessons

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

Practical ML Techniques/Concepts

6 Lessons

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

Search Ranking

8 Lessons

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

Feed Based System

9 Lessons

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

Recommendation System

7 Lessons

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

Self-Driving Car: Image Segmentation

5 Lessons

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

Problem Statement

Metrics

Architectural Components

Training Data Generation

Modeling

Entity Linking System

5 Lessons

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

Problem Statement

Metrics

Architectural Components

Training Data Generation

Modeling

Ad Prediction System

7 Lessons

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

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Machine Learning Labs

Developed by MAANG Engineers

Every Educative resource is designed by our in-house team of ex-MAANG software engineers and PhD computer science educators — subject matter experts who’ve shipped production code at scale and taught the theory behind it. The goal is to get you hands-on with the skills you need to stay ahead in today's constantly evolving tech landscape. No videos, no fluff — just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

Trusted by 2.7 million developers working at companies

"Educative has been instrumental in accelerating my learning by offering interactive, text-based courses that helped me master complex topics like algorithms, system design, and programming languages."

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Learner

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Learner

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Learner

Hands-on Learning Powered by AI

See how Educative uses AI to make your learning more immersive than ever before.

Personalized Interview Prep

Skip the LeetCode grind with a custom roadmap that adapts to your goals. Hands-on practice for Coding Interviews, System Design, and more.

Mock Interviews

Test your skills in a simulated interview setting. Receive personalized feedback based on your performance. Available for Coding Interviews, System Design, and more.

AI Prompt

Build prompt engineering skills. Practice implementing AI-informed solutions.

Code Feedback

Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

Explain with AI

Select any text within any Educative course, and get an instant explanation — without ever leaving your browser.

AI Code Mentor

AI Code Mentor helps you quickly identify errors in your code, learn from your mistakes, and nudge you in the right direction — just like a 1:1 tutor!

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Free Resources

Frequently Asked Questions

How do I prepare for a machine learning interview?

In order to prepare for a machine learning interview, developers should focus on key topics like algorithms, data preprocessing, model evaluation, and common frameworks. The next step follows: practicing coding problems, reviewing machine learning concepts, and building projects.

What are machine learning interviews?

Machine Learning (ML) interviews judge your knowledge of machine learning frameworks such as TensorFlow and Scikit-learn, and core concepts related to the company’s field. You might also be asked to design an ML system or pipeline while keeping certain specifications in mind. Developers looking to prepare for machine learning interviews should take courses in grokking the machine learning interview.

What are the 4 basics of machine learning?

The four basics of machine learning are as follows:

Data: Models learn patterns and make predictions based on data.
Algorithms: These are the techniques used to process data and learn from it.
Model: A mathematical representation that is used to make predictions.
Training: The process of feeding data into a model to learn patterns.