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Grokking the Machine Learning Interview

Name: Grokking the Machine Learning Interview
Rating: 5 (3 reviews)

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

Intermediate

49 Lessons

15h

Certificate of Completion

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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Explanations

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Mock Interview

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Course Overview

Course Content

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Course Overview

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 ...Show More

Course Content

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

Course Author

Machine Learning Labs

Project

Predict Cancer Using Machine Learning Models

advanced

2 hour

Project

Predict Cancer Using Machine Learning Models

Learn to classify cancer mutations into nine categories using gene and variation data, with text cleaning, feature importance analysis and ML model comparisons.

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2 hours

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Instant 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.

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Put your skills to the test in a simulated interview setting. Receive personalized feedback based on your performance. Available in Premium & Premium Plus plans.

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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.