Here's how you can ace machine learning interview questions by avoiding common mistakes.

Know the basics to appreciate the complexity (cost, infrastructure, code, etc.) associated with developing even a simple AI/ML system. Develop and work on end-to-end MLOps. This will allow us to understand why only companies like Google, Microsoft, Facebook, or OpenAI can create and compete in the GenAI space. If you start your AI journey directly from GenAI (e.g., ChatGPT), it will be hard to convince others that you know the basics. At work, applications go beyond just writing a clear/correct email or copy-pasting Python code

In interviews, clarity is key. For instance, if asked about "algorithm selection," clarify whether they mean how you decide between decision trees, SVMs, or neural networks based on data characteristics. This shows your thoroughness and ensures you answer precisely what they're asking. Clear communication is crucial in ML roles to explain technical ideas clearly, ensuring everyone understands and improving project success.

Be ready to discuss your past projects in detail. Common mistakes include not being able to explain the problem, the approach, and the results clearly. Be prepared to dive into technical details and discuss challenges you faced and how you overcame them.

Explaining your code in a machine learning interview is not just about technical skills; it's about storytelling your journey of problem-solving and showcasing your ability to innovate and adapt. Start by introducing the goal of your code and why it matters. Share why you picked certain algorithms or methods and how you prepared your data to make it usable. Describe how you trained your model—like teaching it to recognize patterns—and how you measured its success. Talk about any challenges you faced along the way and how you overcame them. Wrap up by discussing what you learned and how you'd improve your model next time.

If you have worked on real project describe that if not create something that you are passionate about.. it can be anything that u love for example I never get bored with listening to music-- think how that hobby can be converted into AI projects

Project Examples: Discuss specific machine learning projects you’ve worked on. For instance, mention a recommendation system you developed. Challenges and Solutions: Explain the challenges you faced, such as data preprocessing, Data imbalance or model overfitting, and how you overcame them using techniques like cross-validation or hyperparameter tuning. Impact and Results: Highlight the impact of your work, including any metrics or business outcomes improved by your solution. Collaboration and Communication: Showcase your ability to work with cross-functional teams and communicate findings to non-technical stakeholders.

Not always but sometimes Avoid using specific technical words because as an ML expert, you are not only giving interviews to people of the same domain. The initial interview is probably with HR or a non-technical person. You have to communicate with nontechnical people in a way that they can understand your point making communication effective. If the interviewer is of the same domain as yours and asks specific questions then jargon can be an effective way to impress the interviewer.

In machine learning, staying current is crucial. For example, keeping up with new developments in tools like TensorFlow for improving image recognition shows your proactive approach. Engaging in online communities, workshops, newsletters, journals, blogs, helps you learn and share insights, demonstrating your commitment to continuous growth. Interact with people in your network, engage with industry experts. This active involvement not only enhances your skills but also connects you with peers, fostering ongoing learning and innovation in the field.