Google Machine Learning Engineer Interview Tips
Be sure to include the following tips in your prep plan to take preparation to the next level:
What Is Overfitting And How Can You Avoid It
The Overfitting is a situation that occurs when a model learns the training set too well, taking up random fluctuations in the training data as concepts. These impact the models ability to generalize and dont apply to new data.
When a model is given the training data, it shows 100 percent accuracytechnically a slight loss. But, when we use the test data, there may be an error and low efficiency. This condition is known as overfitting.
There are multiple ways of avoiding overfitting, such as:
- Regularization. It involves a cost term for the features involved with the objective function
- Making a simple model. With lesser variables and parameters, the variance can be reduced
- Cross-validation methods like k-folds can also be used
- If some model parameters are likely to cause overfitting, techniques for regularization like LASSO can be used that penalize these parameters
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Seasoned Machine Learning Professionals
With a hefty list of work experience, you need to make sure that your machine learning engineer resume is not overburdened with information.
Here is what you can do to ensure that:
- Split your work experience into a bulleted list of one-liners to enhance readability
- Start each one-liner with power verbs for extra impact to the recruiters
- Maintain a cause-effect relationship in every sentence
- Quantify details such as revenue earned, recruits trained, etc.
Doing so will help you make the most of your work experience in your machine learning engineer resume. Additionally, you need to make sure that you follow the reverse chronological resume format to add transparency.
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Machine Learning Engineer Interview Question #: Regression Definition
While the range of theoretical questions asked to machine learning engineers at technical interviews is very broad, there are a couple of types of questions that come up more frequently. One of them is to explain a certain machine learning concept in very simple terms, such as if we were explaining it to an executive with no technical background or to a child. Belvedere Trading is one of the companies that ask questions such as this to find the words to explain Regression to 8 years old.
Link to the question:
Even for beginner machine learning engineers, it should be clear that the regression is an analysis that we use to predict an unknown event, a continuous value, based on evidence that we’ve gathered in the past. But how to explain a concept like that to a child?
We can provide them with a simple example that can be easily followed like this:
- Let’s say that you want to go to school. You notice that it takes you only 1 minute to reach the school because it’s just across the street.
- Next, you ask your friend, John: how long does it take for him to reach the school? And he says: 20 minutes. When you ask why he answered: because his house is 5 km away from the school.
- Next, you ask your other friend, Andy, the same question. He answered: It takes him 40 minutes to reach the school. Now, if you guess that Andy’s house is more than 5 km away from school, then you’re doing regression analysis.
I Got Google Mle By Practicing 200 Lc Questions
In this interview series, I summarize interview experiences from people I helped. I hope it helps you in preparing for Google MLE interview.
SC has background in Computer Science with 4 years of experience. He got Google Software Engineer, Machine Learning position in East Coast. He wants to share his experience for others.
Only the week before an interview for that company. The frequency tags were fairly accurate for Amazon, Facebook, and Two Sigma but Google used fairly unique problems I havent seen on Leetcode before.
2. How many questions did you solve? How many weeks/hours you spent on LC practice.
All said and done about 200 questions on Leetcode. I feel I over prepared. I focused my efforts on mostly Medium problems and occasionally would blitz 5 easy questions in 30 minutes. Spent some time on hards but not much. I did probably 10 hours a week for 23 months. Just estimating hard to say. Probably did something around 50 Leetcode questions during actual interviews since I spoke with a lot of companies.
3. If you start again what is the one thing you want to do differently in terms of preparation?
I think I did a good job of preparing and I wouldnt change anything.
4. What other tips would you recommend?
5. Do you find mock interviews helpful? In terms of ML knowledge, which books/resources you find helpful.
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What Happens Behind The Scenes
If things go well at your onsite interviews, here is what the final steps of the process look like:
- Interviewers submit feedback
- Senior leader and compensation committee review
- Final executive review
- You get an offer
After your onsite, your interviewers will all submit their feedback usually within two to three days. This feedback will then be reviewed by a hiring committee, along with your resume, internal referrals, and any past work you have submitted. At this stage, the hiring committee will make a recommendation on whether Google should hire you or not.
If the committee recommends that you get hired, you’ll usually start your team matching process. In other words, you’ll talk to hiring managers and one or several of them will need to be willing to take you in their team in order for you to get an offer from the company.
In parallel, the hiring committee recommendation will be reviewed and validated by a senior manager and a compensation committee who will decide how much money you are offered. Finally, if you are interviewing for a senior role, a senior Google executive will review a summary of your candidacy and compensation before the offer is sent to you.
As you’ve probably gathered by now, Google goes to great lengths to avoid hiring the wrong candidates. This hiring process with multiple levels of validations helps them scale their teams while maintaining a high caliber of employee. But it also means that the typical process can spread over multiple months.
What Is Bias And Variance In A Machine Learning Model
Bias in a machine learning model occurs when the predicted values are further from the actual values. Low bias indicates a model where the prediction values are very close to the actual ones.
Underfitting: High bias can cause an algorithm to miss the relevant relations between features and target outputs.
Variance refers to the amount the target model will change when trained with different training data. For a good model, the variance should be minimized.
Overfitting: High variance can cause an algorithm to model the random noise in the training data rather than the intended outputs.
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Machine Learning Engineer Interview Question #: Recommendation System
This machine learning engineer interview question has recently been asked during technical interviews at Meta and is a perfect example of what machine learning engineers may expect to solve. The candidate is given a list of Facebook friends and the list of Facebook pages that users follow. The task is to create a new recommendation system for Facebook. For each Facebook user, we should find pages that this user doesn’t follow, but at least one of their friends does. The output should include the user ID and the page ID that should be recommended to this user.
Link to the question:
As mentioned in the text of the question, to solve this problem, we should use two relatively simple datasets:
- users_friends has two columns and is a list of Facebook friends,
- users_pages also has two columns and is a list of Facebook pages that users follow
This is what the top rows of the users_friends dataset may look like. The first row, for instance, means that a user with ID 1 is a friend of a user with ID 2.
And this is what the top rows of the users_pages dataset may look like. We can find out, for example, that the user with ID 1 follows two pages with IDs 21 and 25.
friends_pages = users_friends.merge
Google And Amazon Interview Questions For Ml Researcher
Google and Amazon- Landing an excellent job here isnt just the luck of the draw. For every budding AI engineer, these tech titans are a dream come true. Any fresh engineering graduate who has left college dreams of working at Google or Amazon in the perfect working environment. But its a matter of preparation. Even if youre an aspiring ML researcher whos super dedicated to the task, you might find yourself struggling. If youre questioning how to crack an interview at Google or Amazon, you have landed at the right place! In this article, we have placed a mixture of Algorithms/Theory, Programming, Company/Industry Specific, General Machine Learning Interest interview questions together with their answers compiled by the industrys best machine learning experts.
Here is a compilation of different categories in one place so that you can get to the information you need more when it comes to machine learning interview questions. Lets get started!
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Google Machine Learning Interview Questions
What are the assumptions of linear regression?
Linear regression is used to understand the relation between features and target . Before we train the model, we need to meet a few assumptions:
Note: the residuals in linear regression are the difference between actual and predicted values.
Write a function find_bigrams to take a string and return a list of all bigrams.
During coding interviews, you will be asked about machine learning problems, but in some cases, they will assess your Python skills by asking you general coding questions. Become an expert Python programmer by taking the Python Programmer career track.
Creating a bigram function is quite easy. You need to use two loops with the zip function.
It is quite easy if you break down the problem and use zip functions.
def bigram: result = for ls in text_list: words = ls.lower.split for bi in zip: result.append return resulttext = print)
Most common types of Activation Functions:
Google Machine Learning Engineer Career Faqs
1. What do Machine Learning Engineers do at Google?
Machine learning engineers are at the front seat of innovation at Google. They use machine learning and deep learning frameworks to solve real-world problems.
2. How is a Google Machine Learning Engineer different from a Google Software Engineer?
Google machine learning engineers are a subset of Google software engineers. Essentially, ML engineers are software engineers specializing in machine learning.
2. What is the timeline for the Google Machine Learning Engineer Interview process?
A typical Google machine learning engineer interview process goes on for 6-8 weeks.
How I Prepare For Interview
- Programming: Leetcode . Read CTCI and EPI occasionally . Though, I didnt start from scratch. I had CP experience from school
- ML: My MS focused on ML, so prepped with my NLP, ML, and DL course material. Lots of great online courses available too. Stanford CS 229, CS 231n, 224n. MIT DL course
- I also read a lot of online blogs. Ive given some links below : machinelearningmastery, colah.github.io, workera.ai, www.mygreatlearning.com, Glassdoor MLE interview questions
- One of my strong points in interviews is great communication. Always I try to keep the interviews more conversation than Q & A types, of course, while demonstrating the technical skills. I had also prepped well, so was quite confident and optimistic in the interviews.
- Chose Google. a dream come true moment. I hope this experience is useful to someone. I especially felt that SWE in ML experiences were lacking, so hopefully, this post adds value there. Best of luck to everyone else prepping! Stay positive 🙂
If you want to support me you can become a Medium user using this link.
How Is Amazon Able To Recommend Other Things To Buy How Does The Recommendation Engine Work
Once a user buys something from Amazon, Amazon stores that purchase data for future reference and finds products that are most likely also to be bought, it is possible because of the Association algorithm, which can identify patterns in a given dataset.
Free Course: Machine Learning Algorithms
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What Are The Different Methods To Split A Tree In A Decision Tree Algorithm
Variance: Splitting the nodes of a decision tree using the variance is done when the target variable is continuous.
Information Gain: Splitting the nodes of a decision tree using Information Gain is preferred when the target variable is categorical.
Gini Impurity: Splitting the nodes of a decision tree using Gini Impurity is followed when the target variable is categorical.
Get Started With Your Machine Learning Interview Prep
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Explain How A System Can Play A Game Of Chess Using Reinforcement Learning
Reinforcement learning has an environment and an agent. The agent performs some actions to achieve a specific goal. Every time the agent performs a task that is taking it towards the goal, it is rewarded. And, every time it takes a step that goes against that goal or in the reverse direction, it is penalized.
Earlier, chess programs had to determine the best moves after much research on numerous factors. Building a machine designed to play such games would require many rules to be specified.
With reinforced learning, we dont have to deal with this problem as the learning agent learns by playing the game. It will make a move , check if its the right move , and keep the outcomes in memory for the next step it takes . There is a reward for every correct decision the system takes and punishment for the wrong one.
Machine Learning Interview Questions: Company/industry Specific
These machine learning interview questions deal with how to implement your general machine learning knowledge to a specific companys requirements. Youll be asked to create case studies and extend your knowledge of the company and industry youre applying for with your machine learning skills.
Q37: What do you think is the most valuable data in our business?
Answer: This question or questions like it really try to test you on two dimensions. The first is your knowledge of the business and the industry itself, as well as your understanding of the business model. The second is whether you can pick how correlated data is to business outcomes in general, and then how you apply that thinking to your context about the company. Youll want to research the business model and ask good questions to your recruiterand start thinking about what business problems they probably want to solve most with their data.
Q38: How would you implement a recommendation system for our companys users?
Answer: A lot of machine learning interview questions of this type will involve the implementation of machine learning models to a companys problems. Youll have to research the company and its industry in-depth, especially the revenue drivers the company has, and the types of users the company takes on in the context of the industry its in.
More reading: How to Implement A Recommendation System?
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Learn About Google’s Culture
Most candidates fail to do this. But before investing tens of hours preparing for an interview at Google, you should take some time to make sure it’s actually the right company for you.
Google is prestigious and it’s therefore tempting to assume that you should apply, without considering things more carefully. But, it’s important to remember that the prestige of a job won’t make you happy in your day-to-day work. It’s the type of work and the people you work with that will.
If you know engineers who work at Google or used to work there, it’s a good idea to talk to them to understand what the culture is like. In addition, we would recommend reading the following resources: