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 Educative.io. 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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🛎️ Check out Grokking the Machine Learning Interview here.

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.

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: Intermediate

49 Lessons

311 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

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

🛎️ Check out Grokking the Machine Learning Interview here.

✨ 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.

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.

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

✅ 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.

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

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

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:

	Single Course	Monthly Subscription	Yearly Subscription
Cost	$79 per year	$59 per month	$21 per month
Access to 170+ courses	❌	✅	✅
Early access to courses	❌	❌	✅
Certificate of Completion (New!)	✅	✅	✅

🛎️ You can explore 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.

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