HCLTech Ai Ml Engineer Interview Questions and Answers

Q1. Difference between Lemmatization and stemming
Ans. 
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'.
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Q1. What is OHE ( one hot encoding)
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OHE is a technique used in machine learning to convert categorical data into a binary format.
• OHE is used to convert categorical variables into a format that can be provided to ML algorithms.

• Each category is represented by a binary vector where only one element is 'hot' (1) and the rest are 'cold' (0).

• For example, if we have a 'color' feature with categories 'red', 'blue', 'green', OHE would represent them as [1, 0, 0],
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Q2. What is conditional probability
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Conditional probability is the likelihood of an event occurring given that another event has already occurred.
• Conditional probability is calculated using the formula P(A|B) = P(A and B) / P(B)

• It represents the probability of event A happening, given that event B has already occurred

• Conditional probability is used in various fields such as machine learning, statistics, and finance
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Q3. What is precision, recall
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Precision and recall are evaluation metrics used in machine learning to measure the performance of a classification model.
• Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

• Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

• Precision is important when the cost of false positives is high, while recall is i...read more
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Q1. What are your strengths and weaknesses?
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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
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Q2. Could you please walk me through your resume?
Ans. 
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
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Q1. Steps of pre-processing
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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
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Q2. What is lemmatization ?
Ans. 
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.
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Discussion on Gradient, SGD, K Mean ++, Silhouette Score, How to Handle High Variation Data,
Coding asked to code KNN, Hyper-Parameter Tuning, Two Difficult Questions on Coding...DSA Based Stumped on Those.

Verdict... Not Selected

Simple Coding No Chat GPT Support Should Be There

Q1. Explain the ML project you recently worked?
Ans. 
Developed a recommendation system for an e-commerce platform using collaborative filtering
• Used collaborative filtering to analyze user behavior and recommend products

• Implemented matrix factorization techniques to improve recommendation accuracy

• Evaluated model performance using metrics like RMSE and precision-recall curves
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Q2. Questions related to ML fundamentals like supervised learning, unsupervised learning, evaluation and ML algorithms
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Q3. Project specific questions
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Q4. Easy-medium coding questions
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Q1. What technologies you are working on?
Ans. 
I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.
• Python programming language

• TensorFlow framework

• scikit-learn library
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Q2. How you will approach on machine learning problem?
Ans. 
I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.
• Understand the problem statement and define the objectives clearly.

• Collect and preprocess the data to make it suitable for training.

• Select a suitable machine learning algorithm based on the problem type (clas...read more
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