Machine Learning Interview Questions | Machine Learning Interview Preparation | Intellipaat

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Preparing for a machine learning interview? This video has got you covered! We've compiled a list of common machine-learning interview questions and answers to help you ace your next interview.
In this video, we'll cover a range of topics, from the basics of machine learning to more advanced concepts. We'll start with questions about supervised and unsupervised learning, and then move on to topics like feature selection, overfitting, and regularization.
We'll also cover common machine learning algorithms, such as linear regression, logistic regression, decision trees, and neural networks. We'll discuss the strengths and weaknesses of each algorithm, and provide examples of how they can be used in real-world applications.
In addition to technical questions, we'll also cover some non-technical questions that you may encounter in a machine-learning interview. We'll discuss questions about your problem-solving approach, your communication skills, and your experience working in a team.
Whether you're a beginner or an experienced machine learning practitioner, this video will provide you with valuable insights and tips to help you succeed in your next interview. So if you want to improve your chances of landing that dream job in machine learning, be sure to watch this video!
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[@sidebarschocolate]

What about technical questions with real solutions using code?

[@Intellipaat]

If you wish to get Personal support Kindly call our course advisors on IND: [phone] US: 1-800-216-8930 (Toll-Free).

[@Sapre07]

Kindly add the uipath course.

[@Intellipaat]

Sure! We'll do it. Meanwhile, Do like, share & subscribe to our channel to get updates on upcoming videos.

[@rishiraj2548]

Thank you

[@Intellipaat]

Most welcome 😊 Do like, share & subscribe to our channel to get updates on upcoming videos.

[@raihanrahman8536]

Principle component analysis

[@Intellipaat]

We appreciate your active participation😊. Subscribe to Intellipaat to know the right answer.

[@divyanshukumar558]

ANSWER OF QUESTION 6 IS WRONG "When a model gets trained with so much data (NOT INADEQUATE), it starts learning from the noise and inaccurate data entries in our data set." "PLEASE IMPROVE THIS ANSWER"

[@Intellipaat]

Sure sir , will work on that.

[@amanmishra5409]

OR WE CAN SAY THAT WHEN OUR MODEL PERFORMS WELL ON TRAINING DATA BUT NOT ON TEST DATA THEN THAT CASE WE CALL AS OVERFITIING(BCZ OUR MODEL BECOME SO COMPLEX )