What do you do if you're unsure how to approach a coding question in a Machine Learning interview?

"A well-posed problem is a half-done solution" Make sure to ask the interviewer any questions you have about the presented problem. Talk about specifics of the issue, like: 1. Types of input needed 2. Problem complexity 3. Curved Cases Questions about ML coding may also raise issues about the use of libraries (if permitted, which ones), the types of inputs and outputs (text, photos, tables, audio, video, etc.), scoring metrics, and the specific task type (classification, regression), among other things. The more insight you have into the issue, the more adeptly you can determine how to tackle it. Don't forget to brush up on your fundamentals as well; if the high-level strategy fails, the base will get you to the finish line.

This is a tip that I use in my interviews. If the question seems hazy, ask the following questions: - Is this the correct dimension of the input tensor? ... - Are these the set of valid model predictions? ... - What are the goals of the use case? Are we focusing on training accuracy, inference speed or lower memory footprint? (Its usually a combination of the above but in an ML problem it is important to understand the priorities.) The above questions clarify the three main pain points of an ML problem: Inputs, Outputs and Goals

If presented with a machine learning coding question, such as developing a model to predict house prices, begin by clarifying any ambiguities. Ask about the expected model type (e.g., regression), the features that should be considered, and constraints on model complexity. This initial step is critical to ensure you understand the scope and specifics of the problem, allowing you to plan your approach more effectively

When you encounter a coding question in a Machine Learning interview and decide to "think aloud," it involves verbalizing your thought process as you work through the problem. This technique lets the interviewer understand your problem-solving approach and reasoning skills. By articulating your thoughts, you can clarify your understanding and identify any misconceptions or gaps in your logic. This also allows the interviewer to offer guidance, ask clarifying questions, or provide hints. Thinking aloud demonstrates your ability to approach complex problems methodically and communicate effectively, crucial skills in a collaborative environment.

This goes for any coding interview. Interviewers don't expect you to churn out answers immediately like a machine (unless its a trading firm). People usually need time to think through the various tools and algorithms to apply to a problem. However, keeping mum here gives an impression that you are stuck, while in reality you might just be exploring the options. Also no matter the problem, I always prefer to start by saying: "That is an interesting problem, let us think through it...". This is a strong statement that helps me because: - It shows confidence and optimism in tackling the problem. - It invites the interviewer to intervene if I go astray - It sets the tone for a conversational interview where I explain my ideas and thoughts.

When tackling a hypothetical machine learning question, like predicting house prices, verbalize your thought process. Discuss your choice of regression models, the significance of features such as location or size, and how they could impact the price. Explain any data preprocessing steps you’d consider and how you would validate your model. Thinking aloud not only shows your problem-solving approach but also allows interviewers to guide you and provide feedback.

In the industry, this process of "breaking it down" is called modularization. Coming up with logical code pieces/functions is a skill. The article focuses on the advantage it brings in writing the code but a major reason why modularization is very popular is because it improves testing. Writing an entire ML pipeline and debugging it all at once, is like finding the source of a leak after building the house! Breaking down the code helps you test the individual parts of the pipeline before assembling it all together :) Its

Approach a machine learning coding question by breaking it down into manageable components. For a house price prediction model, first define the problem—determine the target variable and identify key features. Next, discuss data collection and cleaning strategies, followed by feature selection and model building. Finally, plan how to evaluate the model’s performance. This step-by-step breakdown helps manage complexity and demonstrates organized thinking.

Before writing actual code for the house price prediction model, outline your solution in pseudocode. Start by defining data structures, then sketch out functions for data preprocessing, feature extraction, and model training. Include pseudocode for model validation. This preliminary step not only clarifies your approach but also ensures that you structure your code efficiently when you begin actual implementation.

To illustrate your approach to the house price prediction question, use simple examples. Explain how varying a single feature, like the size of a house, might affect the prediction. Discuss handling outliers or missing data in a straightforward example to show your practical knowledge of data quality issues. Simple examples make complex problems more understandable and demonstrate your grasp of fundamental concepts.

Revisit fundamental machine learning concepts when answering a coding question. For house price prediction, discuss the principles behind the chosen regression model, the importance of training/testing splits, and the metrics for evaluating model accuracy. Demonstrating a strong foundation in ML fundamentals reassures interviewers of your deep understanding and ability to apply these concepts.

In addition to technical skills, consider discussing ethical implications and fairness in your model during the interview. For the house price prediction model, talk about ensuring that the model does not inadvertently propagate biases based on location or other features. Highlighting considerations like these demonstrates your awareness of the broader impacts of ML applications