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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. In my previous role, we had a project with a tight deadline.

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Must-Have Skills for a Machine Learning Engineer

Pickl AI

Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data. Big data tools and Cloud computing platforms have become essential in providing the scalability and processing power required for effective ML workflows.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. Data Normalization and Standardization: Scaling numerical data to a standard range to ensure fairness in model training.

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How to Choose MLOps Tools: In-Depth Guide for 2024

DagsHub

Scikit-learn Scikit-learn is a machine learning library in Python that is majorly used for data mining and data analysis. It also provides tools for model evaluation , including cross-validation, hyperparameter tuning, and metrics such as accuracy, precision, recall, and F1-score.

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Understanding and Building Machine Learning Models

Pickl AI

Cross-Validation: Instead of using a single train-test split, cross-validation involves dividing the data into multiple folds and training the model on each fold. This technique helps ensure that the model generalises well across different subsets of the data.

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Machine Learning Engineer – Role, Salary and Future Insights

Pickl AI

You should be comfortable with cross-validation, hyperparameter tuning, and model evaluation metrics (e.g., For instance, tech companies, financial institutions, and e-commerce platforms often offer higher salaries due to their reliance on complex algorithms and Data Analysis. accuracy, precision, recall, F1-score).

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Large Language Models: A Complete Guide

Heartbeat

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text.