grokking the mechine learning interview blue and pink with brain and diagram background

Is Grokking the Machine Learning Interview by Educative Worth It? [Machine Learning Interview Preparation]

Grokking the Machine Learning Interview is a brand new course on the Educative platform. And with machine learning projected to be a $3.6 billion industry by 2024, it couldn’t have arrived at a better time.

In a typical machine learning interview, you can expect to discuss the following:

✅ machine learning understanding

✅ machine learning system design

✅ coding and problem solving

And more.

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Machine learning interviews aren’t easy.

Typically machine learning problems are open-ended, which can leave engineers struggling.

But with proper machine learning interview preparation, you’ll be confident when creating solutions.

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.

Grokking the Machine Learning Interview

So whether you’re applying at a FAANG or the local startup of your dreams, Grokking the Machine Learning Interview can help.

Now let’s crack open this course and see what it’s all about.

Grokking the Machine Learning Interview

⚠️ Level: Intermediate49 Lessons311 Illustrations

This course kicks off by setting up a machine learning system.

It covers these key steps:

  • scale and latency requirements
  • defining metrics
  • architecting for scale
  • offline building and execution
  • iterative model improvement

And more.

From there, this machine learning interview course goes over practical ML techniques and concepts.

✨ Practical ML Techniques/Concepts

This section covers 6 machine learning concepts:

✅ performance and capacity

✅ embeddings

✅ transfer learning

✅ model debugging and testing

✅ online experimentation

✅ training data collection strategies

Practical ML Techniques/Concepts in Grokking the Machine Learning Interview

You’ll use these concepts for the duration of the course.

✨ Machine Learning Interview Problems

Here’s where things start to heat up. 🔥

There are 6 problems in this course:

🔷 Search Ranking

🔷 Self-Driving Car: Image Segmentation

🔷 Feed Based System

🔷 Recommendation System

🔷 Entity Linking System

🔷 Ad Prediction System

Each prompt includes:

  • clarifying questions
  • metrics and caveats
  • architectural components
  • training data generation

And more, depending on the problem.

Let’s take a closer look.

i. Search Ranking

Prompt: Design a search relevance system for a search engine.

From here, you’ll clarify the problem by specifying scope, scale and personalization.

Search ranking diagram in Grokking the ML Interview

And then you’ll go over:

document selection – criteria and relevant scoring theme

feature engineering – features specific to searcher, query, document, context

ranking – logistic regression, analyzing performance

filtering results – result set after ranking, building a classifier

And much more.

ii. Feed Based System

Prompt: Design a Twitter feed system that will show the most relevant tweets for a user based on their social graph.

Feed Based System diagram in Grokking the Machine Learning Interview

Then after visualizing and scaling the problem, you’ll learn about:

tweet selection schemes – new & unseen tweets, network & interest/popularity-based tweets

ranking – logistic regression, MART, deep learning, stacking models

diversity – authors and content, repetition penalty

online experimentation – training models, validating models offline, deployment

And more.

iii. Recommendation System

Prompt: Display media recommendations for a Netflix user.

After establishing the typical metrics and architectural components, you’ll look at:

feature engineering – context, media, and user-based features, media-user cross features

candidate generation – collaborative filtering, generate embedding using neural networks/deep learning

Recommendation System diagram in Grokking the Machine Learning Interview

training data generation – generating, weighting and balancing training examples, train test split

ranking – logistic regression, deep neural networking with sparse and dense features, network structure, re-ranking

iv. Self-Driving Car: Image Segmentation

Prompt: Design a self-driving car system focusing on its perception component.

Self-Driving Car: Image Segmentation diagram in Grokking the Machine Learning Interview

Then after establishing metrics and architectural components, you’ll go over:

training data generation – human-labeled data, open source datasets, training data enhancement through GANs, targeted data gathering

modeling – SOTA segmentation models (FCN, U-Net, Mask R-CNN), transfer learning

v. Entity Linking System

Prompt: Design an entity linking system that:

  • identifies potential named entity mentions in the text
  • searches for corresponding entities in the target knowledge base
  • returns the best candidate corresponding entity or nil
Entity Linking System in Grokking the Machine Learning Interview

Then, you’ll go over:

training data generation – open-source data sets, human-labeled data

modeling – contextualized text representation (ELMo and BERT), NER modeling, disambiguation modeling

Entity Linking System diagram in Grokking the Machine Learning Interview

And more.

vi. Ad Prediction System

Prompt: Build a system to show relevant ads to users.

From there, this machine learning interview course helps you visualize the problem.

And then it goes over questions that can narrow the scope of the problem:

feature engineering – ad-specific features, user-specific features, context-specific features, user-ad cross features, etc.

training data generation – online user engagement, model recalibration, train test split, balance positive and negative training examples

ad selection – top relevant ads, ranking ads, scaling

ad prediction – modeling approach, model for online learning, auto non-linear feature generation

And much more.

💰 Cost

You can buy this machine learning interview preparation course for $79.

Or you can opt in for a monthly or yearly subscription to Educative:

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You can check out Grokking the Machine Learning Interview here.

Is Grokking the Machine Learning Interview worth it? Conclusion

Grokking the Machine Learning Interview covers these core concepts:

✅ machine learning understanding

✅ machine learning system design

✅ coding and problem solving

So if you want the best in machine learning interview preparation, then this course is definitely for you.

Up Next: Grokking the Object Oriented Design Interview [Smash Hit Course by Educative]

  1. Is Grokking the Machine Learning Interview worth it?

    Grokking the Machine Learning Interview covers these core concepts: machine learning understanding, machine learning system design, and coding and problem solving. So if you want the best in machine learning interview preparation, then this course is definitely beneficial.

  2. Is there a machine learning interview course?

    Yes. Grokking the Machine Learning Interview is a new course on the Educative platform. Here you'll set up a machine learning system, learn core machine learning techniques and concepts and work on 6 problems. There are hundreds of diagrams to illustrate metrics, architectural components, training data generation, and more.

  3. How can I make machine learning interview preparation easier?

    Courses can be effective for machine learning interview preparation. Grokking the Machine Learning Interview is a new course by Educative. This detailed course contains hundreds of illustrations and diagrams. You'll learn how to set up a machine learning system, learn core techniques and concepts, and work on 6 detailed problems. Each covers topics such as metrics, caveats, architectural components, training data generation, and beyond. These questions will help you prepare for the machine learning interview.