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Machine Learning Engineer Concept Interview Questions

Review this list of 73 Concept Machine Learning Engineer interview questions and answers verified by hiring managers and candidates.
  • Machine Learning Engineer
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  • Google logoAsked at Google 
    5 answers
    +2

    "Deep Learning is a part of Artificial Intelligence, it's like teaching the machine to think and make decisions on its own. It's like how we teach a child the concept of an apple - it's round, red, has a stem on top. We show them multiple pictures of apples and then they understand and can recognize an apple in future. Similarly, we feed lots of data to the machine, and slowly, it starts learning from that data, and can then make relevant predictions or decisions based on what it has learnt. A co"

    Surbhi G. - "Deep Learning is a part of Artificial Intelligence, it's like teaching the machine to think and make decisions on its own. It's like how we teach a child the concept of an apple - it's round, red, has a stem on top. We show them multiple pictures of apples and then they understand and can recognize an apple in future. Similarly, we feed lots of data to the machine, and slowly, it starts learning from that data, and can then make relevant predictions or decisions based on what it has learnt. A co"See full answer

    Machine Learning Engineer
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  • Anthropic logoAsked at Anthropic 
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    Machine Learning Engineer
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  • Reddit logoAsked at Reddit 
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    Machine Learning Engineer
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  • OpenAI logoAsked at OpenAI 
    2 answers

    "Reinforcement Learning is a type of machine learning where an agent learns to make decisions by trying out different actions and receiving rewards or penalties in return. The goal is to learn, over time, which actions yield the highest rewards. There are three core components in RL: The agent — the learner or decision-maker (e.g., an algorithm or robot), The environment — everything the agent interacts with, Actions and rewards — the agent takes actions, and the environmen"

    Constantin P. - "Reinforcement Learning is a type of machine learning where an agent learns to make decisions by trying out different actions and receiving rewards or penalties in return. The goal is to learn, over time, which actions yield the highest rewards. There are three core components in RL: The agent — the learner or decision-maker (e.g., an algorithm or robot), The environment — everything the agent interacts with, Actions and rewards — the agent takes actions, and the environmen"See full answer

    Machine Learning Engineer
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  • Apple logoAsked at Apple 
    2 answers

    "Hey Grandma, you've had a lot of experience with infants, haven't you? When they were babies, you taught them how to chew in their first six months. This initial phase is like giving them data. Once they learned how to chew, they could handle any food you gave them. Next, you refined their learning by teaching them that they should only chew on food. This is like refining the data so they understand what is relevant. Then, a few months later, they started crawling and walking, learning by observ"

    Hari priya K. - "Hey Grandma, you've had a lot of experience with infants, haven't you? When they were babies, you taught them how to chew in their first six months. This initial phase is like giving them data. Once they learned how to chew, they could handle any food you gave them. Next, you refined their learning by teaching them that they should only chew on food. This is like refining the data so they understand what is relevant. Then, a few months later, they started crawling and walking, learning by observ"See full answer

    Machine Learning Engineer
    Concept
  • Nvidia logoAsked at Nvidia 
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    Machine Learning Engineer
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  • Snap logoAsked at Snap 
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    Machine Learning Engineer
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  • Anthropic logoAsked at Anthropic 
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    Machine Learning Engineer
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  • Apple logoAsked at Apple 
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    Machine Learning Engineer
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  • Google logoAsked at Google 
    2 answers

    "Back in the day, my grandma was like a walking encyclopedia. Whether we were strolling in the park or chilling at a picnic, she'd point out every pigeon, golden retriever, and grape in sight & help me learn stuff. But let's be real, it took me a few tries to get those correct. And more than once, I confused a golden retriever for a labrador Computers are just like us. You show them a bunch of examples, they make some mistakes, and then you give them a little feedback in the right direction. Be"

    Adarsh R. - "Back in the day, my grandma was like a walking encyclopedia. Whether we were strolling in the park or chilling at a picnic, she'd point out every pigeon, golden retriever, and grape in sight & help me learn stuff. But let's be real, it took me a few tries to get those correct. And more than once, I confused a golden retriever for a labrador Computers are just like us. You show them a bunch of examples, they make some mistakes, and then you give them a little feedback in the right direction. Be"See full answer

    Machine Learning Engineer
    Concept
  • Machine Learning Engineer
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  • Apple logoAsked at Apple 
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    Machine Learning Engineer
    Concept
  • Snap logoAsked at Snap 
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    Machine Learning Engineer
    Concept
  • Amazon logoAsked at Amazon 
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    Machine Learning Engineer
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  • Nvidia logoAsked at Nvidia 
    1 answer

    "Clarifying When we say cloud gaming, we refer to a video gaming experience using cloud computing, right? Assumption: Yes. Understanding of cloud computing first. I'll use some analogies: Imagine you are looking to do heavy computing but don't have a powerful CPU and GPU. CPU and GPU are like your big calculators. You can buy a powerful CPU and GPU, but problems: It costs a lot to buy. It costs a lot to run. You don't need it 24-7. You are not a un"

    Darpan D. - "Clarifying When we say cloud gaming, we refer to a video gaming experience using cloud computing, right? Assumption: Yes. Understanding of cloud computing first. I'll use some analogies: Imagine you are looking to do heavy computing but don't have a powerful CPU and GPU. CPU and GPU are like your big calculators. You can buy a powerful CPU and GPU, but problems: It costs a lot to buy. It costs a lot to run. You don't need it 24-7. You are not a un"See full answer

    Machine Learning Engineer
    Concept
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  • Microsoft logoAsked at Microsoft 
    2 answers

    "BERT - bidirectional encoder representations from transformer. For example:- it takes an entire sentence as input at once and understands the meaning of the words in that sentence and calculate the relations of words with each other irrespective of their positions from the original word to understand the meaning of the word using neighboring words. BERT model is a pre trained transformer model which can be fine-tuned for our purposes. It is used for tasks such sentimental analysis, question answ"

    Bhavya V. - "BERT - bidirectional encoder representations from transformer. For example:- it takes an entire sentence as input at once and understands the meaning of the words in that sentence and calculate the relations of words with each other irrespective of their positions from the original word to understand the meaning of the word using neighboring words. BERT model is a pre trained transformer model which can be fine-tuned for our purposes. It is used for tasks such sentimental analysis, question answ"See full answer

    Machine Learning Engineer
    Concept
  • Machine Learning Engineer
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  • Scale AI logoAsked at Scale AI 
    1 answer

    "A typical computer vision pipeline consists of several key stages that process and analyze visual data to extract meaningful information. Here’s a general outline of the steps involved: Image Acquisition:Capturing images or videos using cameras or other imaging devices. Preprocessing steps such as resizing, cropping, and converting color spaces. Image Preprocessing:Noise reduction (e.g., using filters like Gaussian blur). Image normalization to standardize pixel values. Contrast e"

    Shibin P. - "A typical computer vision pipeline consists of several key stages that process and analyze visual data to extract meaningful information. Here’s a general outline of the steps involved: Image Acquisition:Capturing images or videos using cameras or other imaging devices. Preprocessing steps such as resizing, cropping, and converting color spaces. Image Preprocessing:Noise reduction (e.g., using filters like Gaussian blur). Image normalization to standardize pixel values. Contrast e"See full answer

    Machine Learning Engineer
    Concept
Showing 21-40 of 73
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