Review - Is Grokking the Machine Learning Interview on Educative Worth it in 2025?

Hello friends, we are here again today for another exciting topic to discuss. But, today we are not gonna discuss something which is related to Java or any other language or spring boot. Today we are gonna discuss something which is immensely practical and has the potential to land you very high-paying data science jobs.  Today we are gonna review a course that focuses on Machine Learning! Machine Learning is very important when we are considering data science interviews! So what's the wait? Let's start! On Educative.io, there is a great course called Grokking the Machine Learning Interview. It couldn't have come at a better moment, with machine learning expected to be a $3.6 billion business by 2025.

If you don't know Educative is another online learning platform that is gaining a lot of traction for its text-based, interactive learning courses. Reading is generally faster than watching and If you prefer reading text than watching videos then this is the platform to checkout. 

It has some of the best courses to prepare for coding interviews like Grokking the system design interview and Grokking the Advanced System Design Interview, which are not just great for programming interview but also for Machine learning and Data Science interviews. It also has a lot of free resources like this free JavaScript tutorial to learn essential technologies. 

You may anticipate talking about the following topics in a typical machine learning interview:

• machine-learning comprehension
• design of a machine learning system
• problem-solving and coding

Machine learning issues are typically open-ended, which can be difficult for engineers to solve. However, if you prepare for your machine learning interview properly, you'll be confident in your ability to provide solutions. Grokking the Machine Learning Interview may assist you whether you're applying to a FAANG or the small company of your dreams.

It's crucial to realize that there are two types of machine learning interview questions at large tech organizations (like Facebook and Microsoft, where I've worked):

• "Narrow" questions that assess your knowledge of basic machine learning topics such as bias and variance, supervised vs. unsupervised learning, Bayes' theorem, and so on. The point of this exercise is to see if you truly know your way around ML. These principles can be learned from a variety of sources.
  
  
• ML system design problems that are "open-ended." For example, you may be asked how you would go about developing an ad prediction system, a search ranking system, or a social media newsfeed. The objective is to see if you're capable of "zooming out" and thinking about systems on a larger scale.
  
  Can you consider the benefits and drawbacks of various techniques and explain your thoughts clearly? Apart from Grokking the Machine Learning Interview, there are no other materials I've seen that teach you how to approach machine learning system design interview questions.

Too many people focus too much time and energy on narrow questions and neglect to prepare for ML system design questions, despite the fact that the latter can have a significant impact on hiring level and, as a result, make a significant difference in your salary - in the tens of thousands of dollars.

The authors' major objective was to provide enough material for learners to think through and make ML system design decisions for the kind of open-ended questions they're likely to encounter during their interviews. It assists students in considering requirements, comparing alternatives, and developing solutions that they can confidently defend.

 
Senior ML engineers with industry experience, notably at FAANG firms, designed and peer-reviewed this course. These are engineers who have spent almost as long tackling real-world ML issues as they have to interview other ML engineers. It went through hundreds of changes and comments over the course of a year to arrive at its current state.

 

Is Grokking the Machine Learning Interview on Educative Worth it?

Now, let's see what the course has to offer us:

The first step in this course is to put up a machine learning system.

It includes the following important steps:

• Needs for scale and latency
• architecting for scale defining metrics
• Iterative model improvement through offline development and execution

Following that, this machine learning interview course delves into practical ML ideas and practices.

Six machine learning principles are covered in this section:

1. capacity and performance
2. embedding
3. transferring knowledge
4. Model testing and debugging
5. experimenting on the internet
6. data gathering techniques for training

The problems in this course are offered by educative.io are as listed below:

1. Self-Driving Car: Image Segmentation
2. Feed Based System
3. Recommendation System
4. Entity Linking System
5. Ad Prediction System
6. Search Ranking
   
   

Understanding Machine Learning, Machine Learning System Design, and Coding and Problem Solving are covered in depth in Grokking the Machine Learning Interview. So, if you're looking for the greatest machine learning interview preparation, this course is a must.

Educative is also offering now whopping 50% discount on their yearly subscription. As a programmer, there is a lot to learn and sometimes you want the simplicity of taking multiple courses without paying for each one.  With Educative Unlimited subscription, you can now just pay once and get a full access to every course on Educative. 

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