Here's how you can ace Machine Learning job interviews.

Fundamental Ideas- Interviewers are likely to test fundamentals - read about machine learning vs traditional approaches, supervised vs unsupervised learning, regression vs classification, train and validation data, loss function, model training and evaluation, etc. ML Algorithms- Pick a simple algorithm like Linear Regression and understand how it works, where it is applied, how it's error is minimized, assumptions about the data, in which situations it might fail, etc. Be aware of parametric and non-parametric algorithms. Statistics- Understand the measures of central tendency and spread, (mean, median, mode, range, standard deviation, IQR), normal distribution, conditional probability, independent events, p-value, etc.

For AI basics, you can walk interviewers through a simple multi-layer perceptron (MLP) example, describing how it classifies apples and oranges in a supervised manner. What I'd also add: - "Our MLP consists of an input layer, one or more hidden layers, and finally, an output layer. Each node in a layer, called a neuron, receives input from the previous layer, applies a weighted sum, and passes it through a non-linear activation function." - "The learning algorithm of choice is using backpropagation and a variant of stochastic gradient descent. We calculate error gradients for each label and update the model's parameters iteratively. We repeat this process through multiple iterations, known as epochs, until the error converges."

Show projects where you deploy your ml models. Today it is very easy using tools and modules such as Streamlit. The end goal in these projects is to let others use what you built.

If you don't have a portfolio ready, consider creating several GitHub repositories. You could play around with online product recommender using collaborative filtering or deep learning models or image-base disease detection with CNNs, for example. Kaggle competitions as well as simple real-world use cases can serve as additional inspiration here.

If you're asked to whiteboard for your ML solution, no panic. Not the syntax is important here, but your step-by-step approach to considering functions to implement and describe. If you're asked to code in IDE, you will probably have Terminal-like experience so no focus on fancy functions but coming back to basics, including data structures (arrays, lists, dictionaries) and algorithms (sorting, searching, etcetera). I'd recommend utilizing free minutes with well-known online coding platforms like LeetCode, HackerRank, and CodePen to develop your coding abilities in different languages.

You need to be able to explain difficult machine learning concepts in simple terms. The more you can do that, the more you show both you grasp the concepts and you can interact with business stakeholders. Ideally you should be able to explain in plain English concepts most known only by the mathematical definition: as an extreme example, what is a fully connected layer in a neural network. In a normal interview you definitely need to know how to translate paper symbols into code: for example if in the code the variable is X_train, you need to find yourself what's the equivalent in a formula in a research paper. It seems simple, but many in an interview environment may fail to 'translate' code names into papers' formulas names.

I'd ask myself what is your 1-sentence takeaway from each ML algorithm and its real-world application? For instance, Logistic Regression is commonly used for classification problems like predicting customer churn or loan default; Decision Trees are often used for identifying important features and building rule-based systems; Random Forest is widely used for feature importance analysis and anomaly detection due to its robustness; SVMs excel in high-dimensional spaces and are commonly applied in text classification and image recognition; KNN is useful for recommendation systems and anomaly detection; GANs have shown promising results in NLP tasks like text generation and style transfer.

Learn how to explain complex data processes to non technical people, learn how to sell yourself and your skills to hiring companies or potential clients and learn how to market yourself. Moreover, soft skills may make you be more likable in the interviews.

An interview is definitely a conversation, not just a one-way showcase of your technical skills. The interviewers are short on time, so the best approach is to listen attentively to their questions and resist the urge to immediately jump into demonstrating your expertise. Show that you can actively listen and truly understand the interviewer's perspective and the needs of the role. Then, share examples of how you've collaborated with cross-functional teams in the past and addressed the concerns of diverse stakeholders. Highlight instances where you were able to put yourself in the shoes of your teammates or clients.