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Artículo: AMZ-B0G7Z3Y5N8
Decoding Machine Learning Interviews: Collection of 100 Machine Learning Questions with Answers
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Paperback
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0.39 kg
0.39 kg
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Amazon
Amazon
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USA
Sobre este producto
- Machine learning has become a core competency for modern data science, and today’s aspiring data scientists and machine learning engineers are expected to have a strong grasp of ML foundations. ‘Decoding Machine Learning Interviews’ has 100 questions covering the most commonly interviewed upon topics in foundational ML and is designed to equip you with the knowledge, and confidence needed to excel in technical interviews for data science and machine learning roles.The questions covered in this book span across critical topics including:Statistics for Data Science: Basic statistical methods and inferential techniques that form the backbone of data-driven decision-making in machine learning.Foundational Machine Learning: Basic concepts that every data scientist should know, before diving deeper into specific modelsLinear Regression: The foundations of predictive modeling, asking questions on how linear relationships can be modeled and used for continuous target prediction.Logistic Regression: Extending regression to classification problems, this section tests the key aspects of binary and multi-class classification.Tree-based Models: Questions around decision trees, random forests, and boosting techniques, often pivotal for structured data problems.k-Nearest Neighbors (k-NN): A set of common questions on one of the simplest, yet effective, instance-based learning methods.k-Means and Clustering: Common questions on techniques for unsupervised learning, helping to uncover hidden patterns in data.Support Vector Machines (SVMs): Questions on one of the most robust techniques for classification and regression in high-dimensional spaces.Deep Learning: Most frequently asked questions around neural networks and deep learning, with a special focus on NLP given the Generative AI wave.By the end of this book, you'll have been tested on the most common theoretical concepts asked for Data Science, Applied Science or Machine Learning Engineering roles from an ML perspective.
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