NOV Process & Flow Technologies UK Ai Ml Engineer Interview Questions and Answers

Pre-processing steps involve cleaning, transforming, and preparing data for machine learning models.

• Data cleaning: removing missing values, duplicates, and outliers

• Data transformation: scaling, encoding categorical variables, and feature engineering

• Data normalization: ensuring all features have the same scale

• Data splitting: dividing data into training and testing sets

Lemmatization is the process of reducing words to their base or root form.

• Lemmatization helps in standardizing words for analysis.

• It reduces inflected words to their base form.

• For example, 'running' becomes 'run' after lemmatization.

I excel in problem-solving and collaboration, but I sometimes struggle with time management under tight deadlines.

• Strength: Strong analytical skills - I enjoy dissecting complex problems, as demonstrated in my recent project optimizing a machine learning model.

• Strength: Team player - I thrive in collaborative environments, having successfully led a team to develop a predictive analytics tool.

• Weakness: Time management -...read more

Experienced AI/ML Engineer with a strong background in data science, machine learning, and software development.

• Graduated with a Master's in Computer Science, focusing on machine learning algorithms.

• Worked at XYZ Corp, where I developed a predictive model that improved sales forecasting accuracy by 30%.

• Contributed to an open-source project on GitHub, enhancing a popular ML library with new features.

• Completed an interns...read more

Developed a sentiment analysis model using NLP to analyze customer reviews for a product.

• Collected and preprocessed text data from various sources

• Performed tokenization, stopword removal, and lemmatization

• Built a machine learning model using techniques like TF-IDF and LSTM

• Evaluated the model's performance using metrics like accuracy and F1 score

• Deployed the model for real-time sentiment analysis of new reviews

Cosine similarity is a measure of similarity between two non-zero vectors in an inner product space.

• It measures the cosine of the angle between the two vectors.

• Values range from -1 (completely opposite) to 1 (exactly the same).

• Used in recommendation systems, text mining, and clustering algorithms.

Iterator is an object that allows iteration over a collection, while iterable is an object that can be iterated over.

• Iterator is an object with a next() method that returns the next item in the collection.

• Iterable is an object that has an __iter__() method which returns an iterator.

• Example: List is iterable, while iter(list) returns an iterator.

Python function to calculate cosine similarity between two vectors.

• Define a function that takes two vectors as input.

• Calculate the dot product of the two vectors.

• Calculate the magnitude of each vector and multiply them.

• Divide the dot product by the product of magnitudes to get cosine similarity.

F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall.

• F1 score is the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall).

• It is a better metric than accuracy when dealing with imbalanced datasets.

• A high F1 score indicates a model with both high precision and high recall.

• F1 score ranges from 0 to 1, where

Lemmatization produces the base or dictionary form of a word, while stemming reduces words to their root form.

• Lemmatization considers the context and meaning of the word, resulting in a valid word that makes sense.

• Stemming simply chops off prefixes or suffixes, potentially resulting in non-existent words.

• Example: Lemmatization of 'better' would result in 'good', while stemming would reduce it to 'bet'.